clean up

figs/music/OUlogo.ai

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templates/baposter-template/examples/shrec/Makefile

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-ALL: poster.pdf poster_landscape.pdf 
-
-%.pdf: %.tex Makefile
-	pdflatex $< &&	pdflatex $< && pdflatex $<
-
-clean:
-	  rm -f *.aux	*.bbl	*.blg	*.log	poster.pdf poster_landscape.pdf

templates/baposter-template/examples/shrec/impostor.tex

@@ -1,121 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktextfalse
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
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-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
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-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
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-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(6242.40,1512.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(129,72){\makebox(0,0)[r]{\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(129,282){\makebox(0,0)[r]{\strut{} 0.05}}%
-      \csname LTb\endcsname%
-      \put(129,492){\makebox(0,0)[r]{\strut{} 0.1}}%
-      \csname LTb\endcsname%
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-      \csname LTb\endcsname%
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-      \csname LTb\endcsname%
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-      \put(3185,1242){\makebox(0,0){\strut{}}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(1209,1179){\makebox(0,0)[r]{\strut{}Match}}%
-      \csname LTb\endcsname%
-      \put(1209,999){\makebox(0,0)[r]{\strut{}Impostor}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{impostor}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/poster.aux

@@ -1,5 +0,0 @@
-\relax 
-\bibstyle{ieee}
-\bibcite{amberg07:nonrigid}{1}
-\bibcite{amberg08:recognition}{2}
-\pgfsyspdfmark {pgfid1}{1799790}{816750}

templates/baposter-template/examples/shrec/poster.tex

@@ -1,367 +0,0 @@
-\documentclass[portrait,final,a0paper]{baposter}
-%\documentclass[a4shrink,portrait,final]{baposter}
-% Usa a4shrink for an a4 sized paper.
-
-\tracingstats=2
-
-\usepackage{calc}
-\usepackage{graphicx}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{relsize}
-\usepackage{multirow}
-\usepackage{bm}
-
-\usepackage{graphicx}
-\usepackage{multicol}
-
-\usepackage{pgfbaselayers}
-\pgfdeclarelayer{background}
-\pgfdeclarelayer{foreground}
-\pgfsetlayers{background,main,foreground}
-
-\usepackage{times}
-\usepackage{helvet}
-%\usepackage{bookman}
-\usepackage{palatino}
-
-\newcommand{\captionfont}{\footnotesize}
-
-\selectcolormodel{cmyk}
-
-\graphicspath{{images/}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%% Some math symbols used in the text
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Format 
-\newcommand{\Matrix}[1]{\begin{bmatrix} #1 \end{bmatrix}}
-\newcommand{\Vector}[1]{\Matrix{#1}}
-\newcommand*{\SET}[1]  {\ensuremath{\mathcal{#1}}}
-\newcommand*{\MAT}[1]  {\ensuremath{\mathbf{#1}}}
-\newcommand*{\VEC}[1]  {\ensuremath{\bm{#1}}}
-\newcommand*{\CONST}[1]{\ensuremath{\mathit{#1}}}
-\newcommand*{\norm}[1]{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
-\newcommand*{\abs}[1]{\mathopen| #1 \mathclose|}% use instead of $\|x\|$
-\newcommand*{\absLR}[1]{\left| #1 \right|}% use instead of $\|x\|$
-
-\def\norm#1{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
-\newcommand{\normLR}[1]{\left\| #1 \right\|}% use instead of $\|x\|$
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Multicol Settings
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\setlength{\columnsep}{0.7em}
-\setlength{\columnseprule}{0mm}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Save space in lists. Use this after the opening of the list
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\compresslist}{%
-\setlength{\itemsep}{1pt}%
-\setlength{\parskip}{0pt}%
-\setlength{\parsep}{0pt}%
-}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% Begin of Document
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{document}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% Here starts the poster
-%%%---------------------------------------------------------------------------
-%%% Format it to your taste with the options
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Define some colors
-\definecolor{silver}{cmyk}{0,0,0,0.3}
-\definecolor{yellow}{cmyk}{0,0,0.9,0.0}
-\definecolor{reddishyellow}{cmyk}{0,0.22,1.0,0.0}
-\definecolor{black}{cmyk}{0,0,0.0,1.0}
-\definecolor{darkYellow}{cmyk}{0,0,1.0,0.5}
-\definecolor{darkSilver}{cmyk}{0,0,0,0.1}
-
-\definecolor{lightyellow}{cmyk}{0,0,0.3,0.0}
-\definecolor{lighteryellow}{cmyk}{0,0,0.1,0.0}
-\definecolor{lighteryellow}{cmyk}{0,0,0.1,0.0}
-\definecolor{lightestyellow}{cmyk}{0,0,0.05,0.0}
-
-%%
-\typeout{Poster Starts}
-\background{
-  \begin{tikzpicture}[remember picture,overlay]%
-    \draw (current page.north west)+(-2em,2em) node[anchor=north west] {\includegraphics[height=1.1\textheight]{silhouettes_background}};
-  \end{tikzpicture}%
-}
-
-\newlength{\leftimgwidth}
-\begin{poster}%
-  % Poster Options
-  {
-  % Show grid to help with alignment
-  grid=false,
-  % Column spacing
-  colspacing=1em,
-  % Color style
-  bgColorOne=lighteryellow,
-  bgColorTwo=lightestyellow,
-  borderColor=reddishyellow,
-  headerColorOne=yellow,
-  headerColorTwo=reddishyellow,
-  headerFontColor=black,
-  boxColorOne=lightyellow,
-  boxColorTwo=lighteryellow,
-  % Format of textbox
-  textborder=roundedleft,
-%  textborder=rectangle,
-  % Format of text header
-  eyecatcher=false,
-  headerborder=open,
-  headerheight=0.08\textheight,
-  headershape=roundedright,
-  headershade=plain,
-  headerfont=\Large\textsf, %Sans Serif
-  boxshade=plain,
-%  background=shade-tb,
-  background=plain,
-  linewidth=2pt
-  }
-  % Eye Catcher
-  {\includegraphics[width=10em]{D1077}} % No eye catcher for this poster. (eyecatcher=no above). If an eye catcher is present, the title is centered between eye-catcher and logo.
-  % Title
-  {\sf %Sans Serif
-  %\bf% Serif
-  Expression Invariant Face Recognition with a 3DMM}
-  % Authors
-  {\sf %Sans Serif
-  % Serif
-  \vspace{1em}\{Brian.Amberg, Reinhard.Knothe, Thomas.Vetter\}@unibas.ch
-  }
-  % University logo
-  {% The makebox allows the title to flow into the logo, this is a hack because of the L shaped logo.
-    \makebox[8em][r]{%
-      \begin{minipage}{16em}
-        \hfill
-        \includegraphics[height=2em]{msrlogo}
-        \includegraphics[height=7.0em]{logo}
-      \end{minipage}
-    }
-  }
-
-  \tikzstyle{light shaded}=[top color=baposterBGtwo!30!white,bottom color=baposterBGone!30!white,shading=axis,shading angle=30]
-
-  % Width of left inset image
-     \setlength{\leftimgwidth}{0.78em+8.0em}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% Now define the boxes that make up the poster
-%%%---------------------------------------------------------------------------
-%%% Each box has a name and can be placed absolutely or relatively.
-%%% The only inconvenience is that you can only specify a relative position 
-%%% towards an already declared box. So if you have a box attached to the 
-%%% bottom, one to the top and a third one which should be in between, you 
-%%% have to specify the top and bottom boxes before you specify the middle 
-%%% box.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    %
-    % A coloured circle useful as a bullet with an adjustably strong filling
-    \newcommand{\colouredcircle}[1]{%
-      \tikz{\useasboundingbox (-0.2em,-0.32em) rectangle(0.2em,0.32em); \draw[draw=black,fill=baposterBGone!80!black!#1!white,line width=0.03em] (0,0) circle(0.18em);}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Contribution}{name=contribution,column=0,row=0}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-   {}We introduce a method for expression invariant face recognition. A
-   generative 3D Morphable Model (3DMM) is used to separate identity and
-   expression components. The expression removal results in increased
-   recognition performance, even on difficult datasets, without a decrease in
-   performance on expression-less datasets.
-
-   It is applicable to any kind of input data, and was evaluated here on
-   textureless range scans.
-  \vspace{0.3em}
- }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Model}{name=model,column=0,below=contribution}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    The Model was learnt from 275 subjects. We used one neutral expression scan
-    per identity and 150 expression scans of a subset of the subjects. 
-
-    The identity model is a linear model build from the neutral scans.
-    \begin{align}
-      \VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n\qquad.
-    \end{align}
-    For each of the 150 expression scans, we calculated an expression vector as
-    the difference between the expression scan and the corresponding neutral
-    scan of that subject.  This data is already mode-centered, if we regard the
-    neutral expression as the natural mode of expression data. From these offset
-    vectors an additional expression matrix $\MAT M_e$ was calculated, such that the complete linear Model is
-    \begin{align}
-      \VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n + \MAT M_e\VEC\alpha_e
-    \end{align}
-    The assumption here is, that the face and expression space are linearly
-    independent, such that each face is represented by a unique set of
-    coefficients.  
-  \vspace{0.3em}
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Fitting}{name=fitting,column=0,below=model}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    A Robust Nonrigid ICP method was used to fit the model to the data.
-    Robustness was achieved by iteratively reweighting the correspondences and
-    using hard compatability test for the closest points.
-
-    Fitting was initialized by a simple nose detector and proceeded fully
-    automatic.
-  \vspace{0.3em}
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Distance Measure}{name=measure,column=0,below=fitting}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-   The Mahalanobis angle between the identity coefficients $\VEC{\alpha_{n}}$
-   was used for classification.
-  \vspace{0.3em}
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Expression Neutralization}{name=results neutralization,column=1,row=0}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \begin{tikzpicture}[x=0.3333\linewidth,y=-0.42\linewidth]
-    \path [use as bounding box] (-0.5,-0.5) rectangle(2.5,1.7);
-    \path
-    (0,0) node{\includegraphics[width=0.42\linewidth]{D1077}}
-    (1,0) node{\includegraphics[width=0.47\linewidth]{D1077_fit_expression}}
-    (2,0) node{\includegraphics[width=0.47\linewidth]{D1077_fit}}
-
-    (0,1) node{\includegraphics[width=0.42\linewidth]{D1360}}
-    (1,1) node{\includegraphics[width=0.47\linewidth]{D1360_fit_expression}}
-    (2,1) node{\includegraphics[width=0.47\linewidth]{D1360_fit}}
-
-    (0,1.6) node {\smaller a) Target}
-    (1,1.6) node {\smaller b) Fit}
-    (2,1.6) node {\smaller c) Normalized};
-  \end{tikzpicture}
-  \vspace{0.5em}
-
-  Expression normalisation for two scans of the same individual.  
-  The robust fitting gives a good estimate (b) of the true face surface given
-  the noisy measurement (a). It fills in holes and removes artifacts using
-  prior knowledge from the face model. The pose and expression normalized faces
-  (c) are used for face recognition.
-  \vspace{0.5em}
-  }
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Funding}{name=funding,column=1,span=2,above=bottom}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \smaller 
-  \hspace{1em}This work was supported in part by Microsoft Research through the European PhD Scholarship Programme.
-  }
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Results}{name=results,column=1,span=2,below=results neutralization,above=funding}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-      \begin{multicols}{2}
-        The method was evaluated on the GavabDB expression dataset which
-        contains 427 Scans, with 3 neutral scans and 4 expression scans per ID.
-        To test the impact of expression invariance on neutral data we used the
-        UND Dataset from the Face Recognition Great Vendor Test, which contains
-        953 neutral scans with one to eight scans per subject.
-      \end{multicols}\vspace{-1em}
-      \mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
-      \begin{tabular}{cc}
-        \hspace{-0.5em}\scalebox{0.735}{\input{shrec_mncg}} &
-        \hspace{0.5em}\scalebox{0.735}{\input{und_mncg}}
-      \end{tabular}\\
-%      \begin{multicols}{2}
-        {Expression neutralization improves results on the expression dataset
-        without decreasing the accuracy on the neutral testset. Plotted is the
-        ratio of correct answers to  the number of possible correct answers.
-        %Note the different scales for the two graphs.
-        %Our approach has a high accuracy on the neutral (UND) dataset.
-        }
-%      \end{multicols}\vspace{-1em}
-      \\\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
-      \begin{tabular}{cc}
-        \hspace{-0.5em}\scalebox{0.735}{\input{shrec_pr}} &
-        \hspace{0.5em}\scalebox{0.735}{\input{und_pr}}
-      \end{tabular}\\
-%      \begin{multicols}{2}
-        {Plotted are precision and recall for different retrieval depths. The lower
-        precision of the UND database is due to the fact that some queries have no
-        correct answers.}
-%      \end{multicols}\vspace{-1em}
-      \\\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
-      \begin{tabular}{cc}
-        \hspace{-0.5em}\scalebox{0.735}{\input{shrec_far_frr}} &
-        \hspace{0.5em}\scalebox{0.735}{\input{und_far_frr}}
-      \end{tabular}\\
-%      \begin{multicols}{2}
-        {Impostor detection is reliable, as the minimum distance to a match
-        is smaller than the minimum distance to a nonmatch. }
-%      \end{multicols}
-  \\
-  \vspace{0.5em}
-  }
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Robustness}{name=robustness,column=2,row=0,above=results,span=1}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \begin{tikzpicture}[x=0.3333\linewidth,y=-0.42\linewidth]
-    \path [use as bounding box] (-0.5,-0.5) rectangle(2.5,1.7);
-    \path
-    (0,0) node{\includegraphics[width=0.42\linewidth]{D1160}}
-    (1,0) node{\includegraphics[width=0.42\linewidth]{D1425}}
-    (2,0) node{\includegraphics[width=0.42\linewidth]{D1205}}
-
-    (0,1) node{\includegraphics[width=0.28\linewidth]{D1160_fit_expression}}
-    (1,1) node{\includegraphics[width=0.28\linewidth]{D1425_fit_expression}}
-    (2,1) node{\includegraphics[width=0.28\linewidth]{D1205_fit_expression}}
-
-    (1,0.5) node {\smaller a) Target}
-    (1,1.6) node {\smaller b) Robust Reconstruction};
-  \end{tikzpicture}
-  \vspace{0.5em}
-
-  The reconstruction (b) is robust against scans (a) with artifacts, noise, and
-  holes.
-  
-  This is achieved by a robust iteratively reweighted ICP algorithm and outlier
-  rejection based on angle comparisions between corresponding points.
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{References}{name=references,column=0,above=bottom}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    \smaller
-    \vspace{-0.4em}
-    \bibliographystyle{ieee}
-    \renewcommand{\section}[2]{\vskip 0.05em}
-      \begin{thebibliography}{1}\itemsep=-0.01em
-      \setlength{\baselineskip}{0.4em}
-      \bibitem{amberg07:nonrigid}
-        B.~Amberg, S.~Romdhani, T. Vetter.
-        \newblock {O}ptimal {S}tep {N}onrigid {ICP} {A}lgorithms for {S}urface {R}egistration
-        \newblock In {\em Computer Vision and Pattern Recognition 2007}
-      \bibitem{amberg08:recognition}
-        B.~Amberg, R.~Knothe, T. Vetter.
-        \newblock Expression Invariant Face Recognition with a 3D Morphable Model
-        \newblock In {\em Automated Face and Gesture Recognition 2008}
-      \end{thebibliography}
-  }
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Open Questions}{name=questions,column=0,span=1,below=measure,above=references}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    While the expression and identity space are linearly independent, there is
-    some expression left in the identity model. This is because a ``neutral''
-    face is interpreted differently by the subjects.
-
-    We investigate learning ``pure'' separated models from our the available ``impure'' data.
-  }
-
-\end{poster}
-
-\end{document}

templates/baposter-template/examples/shrec/poster_landscape.aux

@@ -1,5 +0,0 @@
-\relax 
-\bibstyle{ieee}
-\bibcite{amberg07:nonrigid}{1}
-\bibcite{amberg08:recognition}{2}
-\pgfsyspdfmark {pgfid1}{1799790}{816750}

templates/baposter-template/examples/shrec/poster_landscape.tex

@@ -1,331 +0,0 @@
-\documentclass[a0paper,landscape,final]{baposter}
-
-\usepackage{times}
-\usepackage{calc}
-\usepackage{graphicx}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{relsize}
-\usepackage{multirow}
-\usepackage{bm}
-
-\usepackage{graphicx}
-\usepackage{multicol}
-
-\usepackage{pgfbaselayers}
-\pgfdeclarelayer{background}
-\pgfdeclarelayer{foreground}
-\pgfsetlayers{background,main,foreground}
-
-\usepackage{helvet}
-%\usepackage{bookman}
-\usepackage{palatino}
-
-\newcommand{\captionfont}{\footnotesize}
-
-\selectcolormodel{cmyk}
-
-\graphicspath{{images/}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%% Some math symbols used in the text
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Format 
-\newcommand{\Matrix}[1]{\begin{bmatrix} #1 \end{bmatrix}}
-\newcommand{\Vector}[1]{\Matrix{#1}}
-\newcommand*{\SET}[1]  {\ensuremath{\mathcal{#1}}}
-\newcommand*{\MAT}[1]  {\ensuremath{\mathbf{#1}}}
-\newcommand*{\VEC}[1]  {\ensuremath{\bm{#1}}}
-\newcommand*{\CONST}[1]{\ensuremath{\mathit{#1}}}
-\newcommand*{\norm}[1]{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
-\newcommand*{\abs}[1]{\mathopen| #1 \mathclose|}% use instead of $\|x\|$
-\newcommand*{\absLR}[1]{\left| #1 \right|}% use instead of $\|x\|$
-
-\def\norm#1{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
-\newcommand{\normLR}[1]{\left\| #1 \right\|}% use instead of $\|x\|$
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Multicol Settings
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\setlength{\columnsep}{0.7em}
-\setlength{\columnseprule}{0mm}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Save space in lists. Use this after the opening of the list
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\compresslist}{%
-\setlength{\itemsep}{1pt}%
-\setlength{\parskip}{0pt}%
-\setlength{\parsep}{0pt}%
-}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% Begin of Document
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{document}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% Here starts the poster
-%%%---------------------------------------------------------------------------
-%%% Format it to your taste with the options
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\typeout{Poster Starts}
-\background{
-  \begin{tikzpicture}[remember picture,overlay]%
-    \draw (current page.north west)+(-2em,-0em) node[anchor=north west] {\hspace{-2em}\includegraphics[height=1.1\textheight]{silhouettes_background}};
-  \end{tikzpicture}%
-}
-\definecolor{silver}{cmyk}{0,0,0,0.3}
-\definecolor{yellow}{cmyk}{0,0,0.9,0.0}
-\definecolor{reddishyellow}{cmyk}{0,0.22,1.0,0.0}
-\definecolor{black}{cmyk}{0,0,0.0,1.0}
-\definecolor{darkYellow}{cmyk}{0,0,1.0,0.5}
-\definecolor{darkSilver}{cmyk}{0,0,0,0.1}
-
-\definecolor{lightyellow}{cmyk}{0,0,0.3,0.0}
-\definecolor{lighteryellow}{cmyk}{0,0,0.1,0.0}
-\definecolor{lighteryellow}{cmyk}{0,0,0.1,0.0}
-\definecolor{lightestyellow}{cmyk}{0,0,0.05,0.0}
-\begin{poster}{
-  % Show grid to help with alignment
-  grid=false,
-  % Column spacing
-  colspacing=1em,
-  % Color style
-  bgColorOne=lighteryellow,
-  bgColorTwo=lightestyellow,
-  borderColor=reddishyellow,
-  headerColorOne=yellow,
-  headerColorTwo=reddishyellow,
-  headerFontColor=black,
-  boxColorOne=lightyellow,
-  boxColorTwo=lighteryellow,
-  % Format of textbox
-  textborder=roundedleft,
-  % Format of text header
-  eyecatcher=false,
-  headerborder=open,
-  headerheight=0.08\textheight,
-  headershape=roundedright,
-  headershade=plain,
-  headerfont=\Large\textsf, %Sans Serif
-  boxshade=plain,
-%  background=shade-tb,
-  background=plain,
-  linewidth=2pt
-  }
-  % Eye Catcher
-  {} % No eye catcher for this poster. If an eye catcher is present, the title is centered between eye-catcher and logo.
-  % Title
-  {\sf %Sans Serif
-  %\bf% Serif
-  Expression Invariant Face Recognition using a 3D Morphable Model}
-  % Authors
-  {\sf %Sans Serif
-  % Serif
-  Brian Amberg\hspace{3em}
-  brian.amberg@unibas.ch\hspace{3em}
-  University of Basel, Switzerland
-  }
-  % University logo
-  {{\begin{minipage}{16em}
-    \hfill
-    \includegraphics[height=2em]{msrlogo}
-    \includegraphics[height=5.5em]{logo}
-  \end{minipage}}
-  }
-
-  \tikzstyle{light shaded}=[top color=baposterBGtwo!30!white,bottom color=baposterBGone!30!white,shading=axis,shading angle=30]
-
-  % Width of left inset image
-     \newlength{\leftimgwidth}
-     \setlength{\leftimgwidth}{0.78em+8.0em}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% Now define the boxes that make up the poster
-%%%---------------------------------------------------------------------------
-%%% Each box has a name and can be placed absolutely or relatively.
-%%% The only inconvenience is that you can only specify a relative position 
-%%% towards an already declared box. So if you have a box attached to the 
-%%% bottom, one to the top and a third one which should be in between, you 
-%%% have to specify the top and bottom boxes before you specify the middle 
-%%% box.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    %
-    % A coloured circle useful as a bullet with an adjustably strong filling
-    \newcommand{\colouredcircle}[1]{%
-      \tikz{\useasboundingbox (-0.2em,-0.32em) rectangle(0.2em,0.32em); \draw[draw=black,fill=baposterBGone!80!black!#1!white,line width=0.03em] (0,0) circle(0.18em);}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Contribution}{name=contribution,column=0,row=0}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-   {}We introduce a method for expression invariant face recognition. A
-   generative 3D Morphable Model (3DMM) is used to separate identity and
-   expression components. The expression removal results in greatly increased
-   recognition performance, even on difficult datasets, without a decrease in
-   performance on expression-less datasets.
-
-   It is applicable to any kind of input data, and was evaluated here on
-   textureless range scans.
- }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Model}{name=model,column=0,below=contribution}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    The Model was learnt from 175 subjects. We used one neutral expression scan
-    per identity and 50 expression scans of a subset of the subjects. 
-
-    The identity model is a linear model build from the neutral scans.
-    \begin{align}
-      \VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n\qquad.
-    \end{align}
-    For each of the 50 expression scans, we calculated an expression vector as
-    the difference between the expression scan and the corresponding neutral
-    scan of that subject.  This data is already mode-centered, if we regard the
-    neutral expression as the natural mode of expression data. From these offset
-    vectors an additional expression matrix $\MAT M_e$ was calculated, such that the complete linear Model is
-    \begin{align}
-      \VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n + \MAT M_e\VEC\alpha_e
-    \end{align}
-    The assumption here is, that the face and expression space are linearly
-    independent, such that each face is represented by a unique set of
-    coefficients.  
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Fitting}{name=fitting,column=0,below=model}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    A Robust Nonrigid ICP method was used to fit the model to the data.
-    Robustness was achieved by iteratively reweighting the correspondences and
-    using hard compatability test for the closest points.
-
-    Fitting was initialized by a simple nose detector and proceeded fully
-    automatic.
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Distance Measure}{name=measure,column=0,below=fitting,above=bottom}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-   The Mahalanobis angle between the identity coefficients $\VEC{\alpha_{n}}$
-   was used for classification.
-  }
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Expression Neutralization}{name=results neutralization,column=1,row=0}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
-    \includegraphics[height=0.42\linewidth]{16_1_tgt}&
-    \includegraphics[height=0.42\linewidth]{16_1_expression}&
-    \includegraphics[height=0.42\linewidth]{16_1_neutral}\\[-0.8em]
-    \smaller a) Target & \smaller b) Fit & \smaller c) Normalized\\[0.8em]
-    \includegraphics[height=0.42\linewidth]{16_6_tgt}&
-    \includegraphics[height=0.42\linewidth]{16_6_expression}&
-    \includegraphics[height=0.42\linewidth]{16_6_neutral}\\[-0.8em]
-    \smaller a) Target & \smaller b) Fit & \smaller c) Normalized
-  \end{tabular}
-  Expression normalisation for two scans of the same individual.  
-  The robust fitting gives a good estimate (b) of the true face surface given
-  the noisy measurement (a). It fills in holes and removes artifacts using
-  prior knowledge from the face model. The pose and expression normalized faces
-  (c) are used for face recognition.
-  }
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Robustness}{name=robustness,column=1,below=results neutralization,span=1,above=bottom}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}}
-    \includegraphics[height=0.42\linewidth]{56_4_tgt}&
-    \includegraphics[height=0.42\linewidth]{23_2_tgt}&
-    \includegraphics[height=0.42\linewidth]{5_6_tgt}\\[-0.8em]
-                       & \smaller a) Targets & \\[0.8em]
-    \includegraphics[height=0.42\linewidth]{56_4_expression}&
-    \includegraphics[height=0.42\linewidth]{23_2_expression}& 
-    \includegraphics[height=0.42\linewidth]{5_6_expression}\\[-0.8em]
-                    & \smaller b) Fits & 
-  \end{tabular}
-  The reconstruction (b) is robust against scans (a) with artifacts, noise, and
-  holes.
-  }
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Results}{name=results,column=2,span=2,row=0}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-      \begin{multicols}{2}
-        The method was evaluated on the GavabDB expression dataset which
-        contains 427 Scans, with 3 neutral scans and 4 expression scans per ID.
-        To test the impact of expression invariance on neutral data we used the
-        UND Dataset from the Face Recognition Great Vendor Test, which contains
-        953 neutral scans with one to eight scans per subject.
-      \end{multicols}\vspace{-1em}
-      \mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
-      \begin{tabular}{cc}
-        \hspace{0.5em}\scalebox{0.74}{\input{shrec_MNCG}} &
-        \hspace{0.5em}\scalebox{0.74}{\input{und_MNCG}}
-      \end{tabular}\\
-%      \begin{multicols}{2}
-        {Expression neutralization improves results on the expression dataset
-        without decreasing the accuracy on the neutral testset. Plotted is the
-        ratio of correct answers to  the number of possible correct answers.
-        %Note the different scales for the two graphs.
-        %Our approach has a high accuracy on the neutral (UND) dataset.
-        }
-%      \end{multicols}\vspace{-1em}
-      \\\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
-      \begin{tabular}{cc}
-        \hspace{0.5em}\scalebox{0.74}{\input{shrec_PR}} &
-        \hspace{0.5em}\scalebox{0.74}{\input{und_PR}}
-      \end{tabular}\\
-%      \begin{multicols}{2}
-        {Plotted are precision and recall for different retrieval depths. The lower
-        precision of the UND database is due to the fact that some queries have no
-        correct answers.}
-%      \end{multicols}\vspace{-1em}
-      \\\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
-      \begin{tabular}{cc}
-        \hspace{0.5em}\scalebox{0.74}{\input{shrec_FARFRR}} &
-        \hspace{0.5em}\scalebox{0.74}{\input{und_FARFRR}}
-      \end{tabular}\\
-%      \begin{multicols}{2}
-        {Impostor detection is reliable, as the minimum distance to a match
-        is smaller than the minimum distance to a nonmatch. }
-%      \end{multicols}
-\\
-  }%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Open Questions}{name=questions,column=2,span=1,above=bottom,below=results}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    While the expression and identity space are linearly independent, there is
-    some expression left in the identity model. This is because a ``neutral''
-    face is interpreted differently by the subjects. We investigate the
-    possibilty to build an identity/expression separated model without using
-    the data labelling, based on a measure of independence.
-  }%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{Funding}{name=funding,column=3,span=1,above=bottom}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \smaller 
-  This work was supported in part by Microsoft Research through the European PhD Scholarship Programme.
-  }%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \headerbox{References}{name=references,column=3,above=funding,below=results}{
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    \smaller
-    \vspace{-0.4em}
-    \bibliographystyle{ieee}
-    \renewcommand{\section}[2]{\vskip 0.05em}
-      \begin{thebibliography}{1}\itemsep=-0.01em
-      \setlength{\baselineskip}{0.4em}
-      \bibitem{amberg07:nonrigid}
-        B.~Amberg, S.~Romdhani, T. Vetter.
-        \newblock {O}ptimal {S}tep {N}onrigid {ICP} {A}lgorithms for {S}urface {R}egistration
-        \newblock In {\em CVPR 2007}
-      \bibitem{amberg08:recognition}
-        B.~Amberg, R.~Knothe, T. Vetter.
-        \newblock Expression Invariant Face Recognition with a 3D Morphable Model
-        \newblock In {\em AFGR 2008}
-      \end{thebibliography}
-  }%
-\end{poster}%
-%
-\end{document}

templates/baposter-template/examples/shrec/precision_recall.tex

@@ -1,121 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
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templates/baposter-template/examples/shrec/residual.table

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templates/baposter-template/examples/shrec/shrec_08.tex

@@ -1,344 +0,0 @@
-\documentclass{vgtc}                          % final (conference style)
-\usepackage{amsmath}
-\usepackage{relsize}
-%\documentclass[review]{vgtc}                 % review
-%\documentclass[widereview]{vgtc}             % wide-spaced review
-%\documentclass[preprint]{vgtc}               % preprint
-%\documentclass[electronic]{vgtc}             % electronic version
-\newif\ifGPblacktext
-\GPblacktexttrue
-
-%% Uncomment one of the lines above depending on where your paper is
-%% in the conference process. ``review'' and ``widereview'' are for review
-%% submission, ``preprint'' is for pre-publication, and the final version
-%% doesn't use a specific qualifier. Further, ``electronic'' includes
-%% hyperreferences for more convenient online viewing.
-
-%% Please use one of the ``review'' options in combination with the
-%% assigned online id (see below) ONLY if your paper uses a double blind
-%% review process. Some conferences, like IEEE Vis and InfoVis, have NOT
-%% in the past.
-
-%% Figures should be in CMYK or Grey scale format, otherwise, colour 
-%% shifting may occur during the printing process.
-
-%% These three lines bring in essential packages: ``mathptmx'' for Type 1 
-%% typefaces, ``graphicx'' for inclusion of EPS figures. and ``times''
-%% for proper handling of the times font family.
-
-\usepackage{bm}
-\usepackage{mathptmx}
-\usepackage{graphicx}
-\usepackage{times}
-
-
-\newcommand*{\SET}[1]  {\ensuremath{\mathcal{#1}}}
-\newcommand*{\MAT}[1]  {\ensuremath{\mathbf{#1}}}
-\newcommand*{\VEC}[1]  {\ensuremath{\bm{#1}}}
-\newcommand*{\CONST}[1]{\ensuremath{\mathit{#1}}}
-\newcommand*{\norm}[1]{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
-\newcommand*{\abs}[1]{\mathopen| #1 \mathclose|}% use instead of $\|x\|$
-\newcommand*{\absLR}[1]{\left| #1 \right|}% use instead of $\|x\|$
-\newcommand*{\normLR}[1]{\left\| #1 \right\|}% use instead of $\|x\|$
-
-%% We encourage the use of mathptmx for consistent usage of times font
-%% throughout the proceedings. However, if you encounter conflicts
-%% with other math-related packages, you may want to disable it.
-
-%% If you are submitting a paper to a conference for review with a double
-%% blind reviewing process, please replace the value ``0'' below with your
-%% OnlineID. Otherwise, you may safely leave it at ``0''.
-\onlineid{0}
-
-%% declare the category of your paper, only shown in review mode
-\vgtccategory{Research}
-
-%% allow for this line if you want the electronic option to work properly
-\vgtcinsertpkg
-
-%% In preprint mode you may define your own headline.
-%\preprinttext{To appear in an IEEE VGTC sponsored conference.}
-
-%% Paper title.
-
-\title{SHREC'08 Entry: Shape Based Face Recognition with a Morphable Model}
-
-%% This is how authors are specified in the conference style
-
-%% Author and Affiliation (single author).
-%%\author{Roy G. Biv\thanks{e-mail: roy.g.biv@aol.com}}
-%%\affiliation{\scriptsize Allied Widgets Research}
-
-%% Author and Affiliation (multiple authors with single affiliations).
-%%\author{Roy G. Biv\thanks{e-mail: roy.g.biv@aol.com} %
-%%\and Ed Grimley\thanks{e-mail:ed.grimley@aol.com} %
-%%\and Martha Stewart\thanks{e-mail:martha.stewart@marthastewart.com}}
-%%\affiliation{\scriptsize Martha Stewart Enterprises \\ Microsoft Research}
-
-%% Author and Affiliation (multiple authors with multiple affiliations)
-\author{Brian Amberg\thanks{e-mail: \{brian.amberg, reinhard.knothe, thomas.vetter\}@unibas.ch} %
-        \and Reinhard Knothe$^*$%
-	\and Thomas Vetter$^*$}%
-
-%% Abstract section.
-\abstract{
-  We present a method for face recognition by fitting a 3D Morphable Model to
-  shape data. Fitting is done with a a robust nonrigid ICP algorithm. For
-  recognition, it is possible to use either the fitted model parameters, or the
-  correspondences induced by the model. We compare different similarity measures,
-  and show that a 3D Morphable Model allows very robust retrieval results.
-} % end of abstract
-
-%% ACM Computing Classification System (CCS). 
-%% See <http://www.acm.org/class/1998/> for details.
-%% The ``\CCScat'' command takes four arguments.
-
-\CCScatlist{ 
-  \CCScat{I.4.8}{Image Processing and Computer Vision}{Scene Analysis}{Object Recognition, Surface Fitting, Range Data}
-  %\CCScat{I.4.7}{Image Processing and Computer Vision}{Feature Measurement}{Size and Shape, Feature representation}
-  I.4.7: Feature Measurement---Size and Shape, Feature representation
-%  \CCScat{I.4.9}{Image Processing and Computer Vision}{Applications}{}
-  I.4.9: Applications
-%  \CCScat{I.4.10}{Image Processing and Computer Vision}{Image Representation}{Statistical}
-  \CCScat{I.5.1}{Pattern Recognition}{Models}{Statistical}
-%  \CCScat{I.5.1}{}{Applications}{Computer Vision}
-}
-
-%% Copyright space is enabled by default as required by guidelines.
-%% It is disabled by the 'review' option or via the following command:
-% \nocopyrightspace
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%% START OF THE PAPER %%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{document}
-
-%% The ``\maketitle'' command must be the first command after the
-%% ``\begin{document}'' command. It prepares and prints the title block.
-
-%% the only exception to this rule is the \firstsection command
-\firstsection{Introduction}
-
-\maketitle
-
-We tackle the task of textureless 3D face recognition. The system is fully
-automatic and can handle the typical artifacts of 3D scanners, namely outliers
-and missing regions. Face recognition in this setting is a difficult task, and
-difficult tasks need strong prior knowledge. To introduce the prior knowledge
-we use a 3D Morphable Model (3DMM)~\cite{blanz:model}, which is a generative
-statistical model of 3D faces. 3DMM have been applied successfully for face
-recognition on different modalities. The most challenging setting is
-recognition from single images under varying light and illumination.  This was
-adressed by~\cite{blanz03:face_rec,romdhani:recognition}. There a 3DMM with
-shape, texture and illumination model was fit to probe and gallery images.  As
-the model separates shape and albedo parameters from pose and lighting, it
-enables pose and lighting invariant recognition.  In~\cite{amberg07:stereo} a
-similar approach was used to fit a pure shape model to stereo images, also
-enabling recognition by correlating the shape parameters. We use the same
-approach for shape based face recognition. We fit a 3DMM build from 170
-subjects with neutral expressions to the \hbox{gavabDB}~\cite{gavabdb} database, and
-compare different distance measures which can be derived from the model fit.
-
-An alternative to fitting a generative model is to align the probe to each
-example in the database using e.g.\ ICP~\cite{bowyer05:icp_recognition}. But
-comparing the probe directly to every gallery image has the disadvantage of
-scaling linearly with the number of entries in the gallery, while for a model
-based approach only a single fit to the probe is necessary, and the comparision
-to the database can then be performed by a distance measure in the lower
-dimensional space of registered faces.
-
-Another interesting model-less approach~\cite{bronstein05:face_rec} compares
-surface by the distribution of geodesics, which stays constant for nonrigidly
-deforming (but not stretching or tearing) objects.  This approach is difficult
-to apply in this setting though, as the scanning produces holes, disconnected
-regions and strong noise, which can best be handled by a method which uses
-specific information about the object class.
-			  
-\section{Fitting}
-\begin{figure}
-  \vspace{-0.5em}
-  \begin{tabular}{@{ }c@{ }c@{ }c@{ }c@{}}
-    \includegraphics[height=0.35\linewidth]{tgt}&
-    \includegraphics[height=0.35\linewidth]{src}&
-    \includegraphics[height=0.35\linewidth]{step}&
-    \includegraphics[height=0.35\linewidth]{extrapolated}\\[-0.8em]
-    \smaller a) Target & \smaller b) Fit & \smaller (a) + (b) & \smaller c) Deformed
-  \end{tabular}
-  \vspace{-1em}
-  \caption{The robust fitting gives a good estimate (b) of the true face surface given
-  the noisy measurement (a). It fills in holes and removes artifacts using prior
-  knowledge from the face model. The fitted shape plus the exact
-  correspondences found can be used to extrapolate the image by a robust
-  poisson deformation (c).}
-  \label{fig:fitting}
-\end{figure}
-The fitting algorithm used in this paper is a variant of the nonrigid ICP work
-in~\cite{amberg07:nicp}. It is a robust iterated fitting algorithm. Like other
-ICP methods, it is a local optimization method, which does not guarantee
-convergence to the global mimimum, but is dependent on the initialization. It
-consists of the following steps
-\begin{itemize}
-  \item Iterate over a sequence of regularization values $\theta_1>\dots>\theta_N$:
-  \begin{itemize}
-  \item Repeat until convergence:
-    \begin{enumerate}
-    \item Find candidate correspondences by searching for the closest compatible
-      point for each model vertex.
-    \item Weight the correspondences by their distance using a robust estimator.
-    \item Fit the 3DMM to these correspondences using a
-      regularization strength of $\theta_i$\label{step_fit}.
-    \item Continue with the lower $\theta_{i+1}$ if the median change in vertex
-      position is smaller than a threshold.
-    \end{enumerate}
-  \end{itemize}
-\end{itemize}
-The search for the closest compatible point takes only points into account which
-have conforming normals. 
-%The search is sped up by organizing the target scan in a space partitioning tree made up of spheres.
-%The robust weighting function used in this paper is
-%\emph{TODO: Check, what I have really done. I kind of hacked it.}
-%\begin{align}
-%  w(r) = \left\{\begin{array}{ll} 1+\frac{m_1-r}{m_1}l_0 & \text{if }r<m_1\\\frac{1}{m_2(r-m_1)+1} &\text{otherwise}\end{array}\right.
-%\end{align}
-%where $m_1$ was set to $3$mm  and $m_2$ to $20$mm. This leads to a robust
-%estimation, which discards outliers. 
-Note, that it is necessary to balance robustness and regularization, as the
-right balance depends on the noise characteristic of the data. Suitable values
-were determined manually for a single scan and kept constant for all
-experiments.  In step~\ref{step_fit} the 3DMM is fit to 3D-3D point
-correspondences.  This is done with a gauss-newton least squares optimization,
-using an analytic Jacobian and first-order Hessian.
-
-As the database is pose normalized, we initialize the registration such that
-the tip of the nose and pose coincides. This initialization is good enough to
-fit the complete database fully automatic. For non pose-normalized databases,
-we would either need three landmarks, or -- to keep the algorithm fully
-automatic -- repeated random initialization.
-
-\section{Retrieval}
-We evaluated four different distance measures, see Figure~\ref{fig:precision} for an overview over their precision recall characteristics.
-
-\subsection{Model Based Measures}
-We begin with measures which are acting in the parameter space of the model.
-These have the advantage of being extremely cheap to calculate, once the model
-has been fit.
-\begin{figure}
-  \hspace{2.5em}\scalebox{0.68}{\input{precision_recall}}
-  \vspace{-0.7em}
-  \caption{Measures in Mahalanobis Space outperform vertex based measures.}
-  \label{fig:precision}
-\end{figure}
-\begin{figure}
-  \vspace{-1.0em}
-  \hspace{2.5em}\scalebox{0.68}{\input{impostor}}
-  \vspace{0.1em}
-  \caption{Impostor detection is reliable, as the minimum distance to a match is smaller than the minimum distance to a nonmatch.}
-  \label{fig:impostor}
-\end{figure}
-
-\subsubsection{Mahalanobis distance of shape coefficients}
-%\paragraph{Mahalanobis distance of shape coefficients}
-The first method calculates the distance between two vectors of shape
-coefficients $\VEC \alpha_1$ and $\VEC\alpha_2$ expressed in Mahalanobis space
-as
-\begin{align}
-  s_1(\VEC\alpha_1, \VEC\alpha_2) &= \norm{\VEC\alpha_1 - \VEC\alpha_2}\qquad.
-\end{align}
-
-\subsubsection{Angular distance of shape coefficients}
-In face space, caricatures lie along the rays from the origin.
-Mapping all caricatured versions of a face onto a canonical face gives a method
-which has proven to have very high recognition rates~\cite{blanz03:face_rec}.
-To do this we use the angle between the shape coefficients in Mahalanobis space
-as the distance measure:
-\begin{align}
-  s_2(\VEC\alpha_1, \VEC\alpha_2) &= \arccos\left(\frac{\VEC\alpha_1^T\VEC\alpha_2}{\norm{\VEC\alpha_1}\norm{\VEC\alpha_2}}\right)\qquad.
-\end{align}
-For the angular measure shown in Figure~\ref{fig:impostor} the distribution of
-distances towards the first match and the first nonmatch in the database. This
-shows, that by choosing a suitable threshold it is possible to perform face
-recognition with impostors, where we decide if the identity is in the database
-or not.
-
-These two measures give similar results, but the caricature invariance of the
-angular distance improves recall a bit. This tells us, that in face space, the
-direction alone codes the identity.
-
-\subsection{Shape Based Measures}
-The second type of measures acts in vertex space. If we want to compare
-two model instances, it does not make much sense to measure in vertex space
-instead of parameter space, as there is a one to one mapping between the spaces
-and the parameter space is of much lower dimensionality. But in our case we do
-have additional information which can not be expressed by the model.
-After fitting, some residual will remain, which is caused by three reasons. 1)
-The individuals used to train the model were not from the gallery, and we can
-not span the complete face space with 170 training examples.  2) The probe
-images have expressions, while the database was build using neutral expressions.
-3) The aquisition process introduces noise. Therefore, we add more
-flexibility to the model by allowing smooth nonrigid deformations of the final
-fit to minimize the remaining residual. This is achieved by robustly fitting a
-poisson deformation with soft boundary and zero right hand side, where the
-boundary is given by the correspondences found by the fitting algorithm, and
-the deformed shape is the fitted head.  Results can be seen in
-Figure~\ref{fig:fitting}.  With these correspondence established we can use
-different distance measures.
-
-\subsubsection{Distance}
-Denote the $N$ vertices of the registered scan $i$ as $\VEC v^i_1,\dots,\VEC
-v^i_N$.  We use the distance after removing the rigid transformation, measuring
-only in a mask defined on the model, which includes the parts which are visible
-in most of the scans. We compute
-\begin{align}
-  s_3(\MAT v^1, \MAT v^2)  &= \min_{\MAT R,\VEC t} \sum_i \norm{\MAT R\VEC v^1_i + \VEC t - \VEC v^2_i}\qquad.
-\end{align}
-where $\MAT R,\VEC t$ describe a similarity transform.
-%
-While this measure is straight forward, the recognition results are
-unsatisfying. The scaling of face space which is learned from the example faces
-results in an improved clustering of scans, which enables better classification
-than comparisions in the original vertex space.
-
-\subsubsection{Geodesics}
-Inspired by~\cite{bronstein05:face_rec} we tried a geodesic based measure,
-which should be invariant against expression changes. We classify by comparing
-the distances of a selected set of vertices, which were assumed not to change
-under expressions.  Denote the selected vertex pairs by $\SET P$. Note that
-this is different from~\cite{bronstein05:face_rec}, as we have already brought
-the meshes into correspondence during fitting. The measure is then
-\begin{align}
-  s_4(\MAT v^1, \MAT v^2) &= \sum_{(i,j)\in \SET P} \absLR{ \norm{v^1_i - v^1_j} - \norm{v^2_i - v^2_j}}
-\end{align}
-Experiments with different sets of distances showed that the best
-classification results were achieved by selecting all neighboring edges of the
-model in the face area. Still, exactly because this method introduces some
-invariance, it also reduces the precision in the retrieval experiments. Also,
-it does not incorporate the knowledge about face space, which was exploited in
-the first two methods.
-
-\section{Conclusion}
-We have shown that 3D Morphable Models provide a valuable tool for face
-recognition with 98.8\% recognition rate on this database. The strong prior
-knowledge allows robust handling of noisy data. Four distance measures
-were compared, and it turns out that the angular distance in Mahalanobis space
-is the most accurate classifier. It was noticably better than measuring
-the difference between the registered scans. This is because the face space
-learned from our training examples is constructed such that the identities are
-better separated than in vertex space.
-
-In the future we wish to make the system expression invariant, by using a
-model which separates expression and identity. As we do establish
-correspondence between the model and the scans, it is trivial to add image
-based classification for datasets where a calibrated photo is available, by
-comparing the rectified textures.
-
-%% if specified like this the section will be ommitted in review mode
-\acknowledgements{The authors wish to thank P.\ Paysan for the data
-acquisition. This work was supported in part by a grant from Microsoft
-Research and the Swiss National Science Foundation (200021-103814 and NCCR COME project 5005-66380).}
-
-\bibliographystyle{abbrv}
-%%use following if all content of bibtex file should be shown
-%\nocite{*}
-\bibliography{shrec_08}
-\end{document}

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-\documentclass[10pt,twocolumn,letterpaper]{article}
-
-\usepackage{relsize}
-\usepackage{fg}
-\usepackage{times}
-\usepackage{epsfig}
-\usepackage{graphicx}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm}
-\usepackage{textcomp}
-\renewcommand*{\d}{\mathrm{d}}
-\newcommand*{\dd}{\partial}
-\newcommand*{\diffp}[2]{\ensuremath{\frac{\dd #1}{\dd #2}}}
-\newcommand*{\diffpp}[3]{\ensuremath{\frac{\dd^2 #1}{\dd #2 \dd #3}}}
-\newcommand*{\diffppp}[4]{\ensuremath{\frac{\dd^3 #1}{\dd #2 \dd #3 \dd #4}}}
-\newcommand*{\difff}[2]{\ensuremath{\frac{\d #1}{\d #2}}}
-\newcommand*{\diffff}[3]{\ensuremath{\frac{\d^2 #1}{\d #2 \d #3}}}
-\newcommand*{\difffp}[3]{\ensuremath{\frac{\dd\d #1}{\d #2 \dd #3}}}
-\newcommand*{\difffpp}[4]{\ensuremath{\frac{\dd^2\d #1}{\d #2 \dd #3 \dd #4}}}
-
-\newcommand{\Matrix}[1]{\begin{bmatrix} #1 \end{bmatrix}}
-\newcommand{\Vector}[1]{\Matrix{#1}}
-\newcommand*{\SET}[1]  {\ensuremath{\mathcal{#1}}}
-\newcommand*{\MAT}[1]  {\ensuremath{\mathbf{#1}}}
-\newcommand*{\VEC}[1]  {\ensuremath{\bm{#1}}}
-\newcommand*{\CONST}[1]{\ensuremath{\mathit{#1}}}
-\newcommand*{\norm}[1]{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
-\newcommand*{\abs}[1]{\mathopen| #1 \mathclose|}% use instead of $\|x\|$
-\newcommand*{\absLR}[1]{\left| #1 \right|}% use instead of $\|x\|$
-\newcommand*{\normLR}[1]{\left\| #1 \right\|}% use instead of $\|x\|$
-% Include other packages here, before hyperref.
-
-% If you comment hyperref and then uncomment it, you should delete
-% egpaper.aux before re-running latex.  (Or just hit 'q' on the first latex
-% run, let it finish, and you should be clear).
-\usepackage[pagebackref=true,breaklinks=true,letterpaper=true,colorlinks,bookmarks=false]{hyperref}
-
-
-\fgfinalcopy % *** Uncomment this line for the final submission
-
-\def\httilde{\mbox{\tt\raisebox{-.5ex}{\symbol{126}}}}
-
-% Pages are numbered in submission mode, and unnumbered in camera-ready
-\iffgfinal\pagestyle{empty}\fi
-\begin{document}
-
-%%%%%%%%% TITLE
-\title{Expression Invariant 3D Face Recognition with a Morphable Model}
-
-\author{Brian Amberg\\
-{\tt\small brian.amberg@unibas.ch} \and
-Reinhard Knothe\\
-{\tt\small reinhard.knothe@unibas.ch} \and
-Thomas Vetter\\
-{\tt\small thomas.vetter@unibas.ch} 
-}
-
-\maketitle
-% \thispagestyle{empty}
-
-%%%%%%%%% ABSTRACT
-\begin{abstract}
-  We present an expression-invariant method for face recognition by fitting an
-  identity/expression separated 3D Morphable Model to shape data. The
-  expression model greatly improves recognition and retrieval rates in the
-  uncooperative setting, while achieving recognition rates on par with the best
-  recognition algorithms in the face recognition great vendor test. The
-  fitting is performed with a robust nonrigid ICP algorithm. It is able to
-  perform face recognition in a fully automated scenario and on noisy data.
-  The system was evaluated on two datasets, one
-  with a high noise level and strong expressions, and the standard UND range
-  scan database, showing that while expression invariance increases recognition
-  and retrieval performance for the expression dataset, it does not decrease
-  performance on the neutral dataset. The high recognition rates are achieved
-  even with a purely shape based method, without taking image data into
-  account.
-\end{abstract}
-
-%%%%%%%%% BODY TEXT
-\section{Introduction}
-We present a system which is using shape information from a 3D scanner to
-perform automated face recognition. The main novelty of the system is its
-invariance to expressions. The system is tested on two
-public datasets. It is fully automatic and can handle the typical artifacts of
-3D scanners, namely outliers and missing regions. Face recognition in this
-setting is a difficult task, and difficult tasks benefit from strong prior knowledge.
-To introduce the prior knowledge we use a 3D Morphable Model
-(3DMM)~\cite{blanz:model}, which is a generative statistical model of 3D faces.
-3DMMs have been applied successfully for face recognition on different
-modalities. The most challenging setting is recognition from single images
-under varying light and illumination.  This was adressed
-by~\cite{blanz03:face_rec,romdhani:recognition}. There, a 3DMM with shape,
-texture and illumination model was fit to probe and gallery images.  As the
-model separates shape and albedo parameters from pose and lighting, it enables
-pose and lighting-invariant recognition. We use the same idea for
-expression-invariant face recognition from 3D shape. We fit an identity/expression
-separating
-3DMM~\cite{blanz03:expression} to shape data and normalize the
-resulting face by removing the pose and expression components. See
-Figure~\ref{fig:fitting} for an example of expression normalization. The
-expression and pose normalized data allows then efficient and effective
-recognition. A 3D MM has been fitted to range data before~\cite{blanz07:range}
-and the results were even evaluated on part of the UND database. Our approach
-differs from this work in the fitting method employed, which is independent of
-the acquisition device, and in the use of an expression model to improve face
-recognition. Additionally, our method is fully automatic,
-while~\cite{blanz07:range} needed seven manually selected landmarks.
-
-\begin{figure}
-  \vspace{-0.5em}
-  \begin{tabular}{@{ }c@{ }c@{ }c@{ }c@{}}
-    \includegraphics[height=0.42\linewidth]{16_1_tgt}&
-    \includegraphics[height=0.42\linewidth]{16_1_expression}&
-    \includegraphics[height=0.42\linewidth]{16_1_neutral}\\[-0.8em]
-    \smaller a) Target & \smaller b) Fit & \smaller c) Normalized\\[0.8em]
-    \includegraphics[height=0.42\linewidth]{16_6_tgt}&
-    \includegraphics[height=0.42\linewidth]{16_6_expression}&
-    \includegraphics[height=0.42\linewidth]{16_6_neutral}\\[-0.8em]
-    \smaller a) Target & \smaller b) Fit & \smaller c) Normalized
-  \end{tabular}
-  \vspace{0.5em}
-  \caption{Expression normalisation for two scans of the same individual.  
-  The robust fitting gives a good estimate (b) of the true face surface given
-  the noisy measurement (a). It fills in holes and removes artifacts using
-  prior knowledge from the face model. The pose and expression normalized faces
-  (c) are used for face recognition.
-  }
-  \label{fig:fitting}
-\end{figure}
-Expression-invariant recognition for shape data was also approached in
-\cite{xiaoguang06:face_matching}, where a person specific 3D Morphable
-Expression Model was learned for each subject in the gallery. In contrast, we
-are using a general 3DMM learned from an independent database of face shapes
-which can be applied without any relearning to a new scan. This makes the
-enrollment phase trivial and the recognition phase effectively constant in the
-size of the gallery while still being accurate. We have to fit just one
-model to the probe, which can then be compared efficiently to
-the enrolled subjects, by comparing their coefficients in the low dimensional
-face space. While the number of comparisions is still at most linear in the
-number of examples (and can be made sublinear with an indexing method) the time
-it takes to compare coefficients in face space is neglectible compared to
-fitting time.
-%
-Model-less approaches which align the probe to each example in the database
-using e.g.\ ICP~\cite{bowyer05:icp_recognition} suffer from the same problem
-as~\cite{xiaoguang06:face_matching}.
-Because the probe has to be aligned with each gallery scan these methods scale
-linearly in the gallery size, While our model based approach needs only a
-single fit to the probe.
-
-Another interesting model-less approach~\cite{bronstein05:face_rec} compares
-surface by the distribution of geodesics, which stays constant for nonrigidly
-deforming (but not stretching or tearing) objects.  This approach is difficult
-to apply in this setting though, as the scanning produces holes, disconnected
-regions and strong noise, which can best be handled by a method which uses
-specific information about the object class.
-			  
-\section{Model}
-A PCA model~\cite{blanz:model} built from 175 subjects was used. It was build
-from one neutral expression face scan per identity and 50 expression scans of a
-subset of the subjects. The data was registered with a modification
-of~\cite{amberg07:nicp}.
-The identity model consists of a mean shape $\VEC\mu$ and a matrix of offset
-vectors $\MAT M_n$ such that a new face instance $\VEC f$ is generated from a
-vector of coefficients $\VEC\alpha_n$ as
-\begin{align}
-  \VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n\qquad.
-\end{align}
-The model is constructed such that the $\alpha_i$ are independently normally
-distributed with zero mean and unit variance under the standard assumption of a
-Gaussian distribution of the data. This was done by performing PCA
-on the data matrix built from the mean free shape vectors.
-Additionally, for each of the 50 expression scans, we calculated an expression
-vector as the difference between the expression scan and the corresponding
-neutral scan of that subject.
-This data is already mode-centered, if we regard the neutral
-expression as the natural mode of expression data. On these offset vectors
-again PCA was applied to get an expression matrix $\MAT M_e$ and 
-expression coefficients $\VEC\alpha_e$, such that the complete expression model is
-\begin{align}
-  \VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n + \MAT M_e\VEC\alpha_e
-         =\VEC\mu + \MAT M\VEC\alpha\qquad,\\
-  \MAT M &= \Matrix{\MAT M_n &|& \MAT M_e} \qquad \VEC\alpha = \Matrix{\VEC\alpha_n \\ \VEC\alpha_e}\qquad.
-\end{align}
-The basic assumption of this paper is, that the face and expression space are
-linearly independent, such that each face is represented by a unique set of
-coefficients.  While the resulting expression and identity matrices are not
-perfectly orthogonal, they do have little overlap, which together with the
-regularisation employed is sufficient for this application. We assume, that the
-overlap between the spaces is due to the fact that it is impossible to aquire
-perfectly consistent neutral expressions.
-
-We use the registered scans and a mirrored version of each registered scan to
-increase the variability of the model. This allows us to calculate a model with
-more than 175 neutral coefficients.
-
-\section{Fitting}
-The fitting algorithm used in this paper is a variant of the nonrigid ICP work
-in~\cite{amberg07:nicp}. The main difference, is that the deformation model is
-a statistical model and the optimisation in each step is an iterative method,
-which finds the minimum of a convex function. Additionally, as it is applied on
-noisy data (see Figure~\ref{fig:difficult}), we included a more elaborate robust weighting term.  Like other
-ICP methods, it is a local optimization method, which does not guarantee
-convergence to the global mimimum, but is dependent on the initialization. It
-consists of the following steps
-\begin{itemize}
-  \item Iterate over regularization values $\theta_1>\dots>\theta_N$:
-  \begin{itemize}
-  \item Repeat until convergence:
-    \begin{enumerate}
-    \item Find candidate correspondences by searching for the closest compatible
-      point for each model vertex.
-    \item Weight the correspondences by their distance using a robust estimator.
-    \item Fit the 3DMM to these correspondences using a
-      regularization strength of $\theta_i$\label{step_fit}.
-    \item Continue with the lower $\theta_{i+1}$ if the median change in vertex
-      position is smaller than a threshold.
-    \end{enumerate}
-  \end{itemize}
-\end{itemize}
-\begin{figure}
-  \vspace{-1.0em}
-  \begin{tabular}{@{ }c@{ }c@{ }c@{ }c@{}}
-    \includegraphics[height=0.42\linewidth]{56_4_tgt}&
-    \includegraphics[height=0.42\linewidth]{23_2_tgt}&
-    \includegraphics[height=0.42\linewidth]{5_6_tgt}\\[-1.0em]
-                       & \smaller a) Targets & \\[0.2em]
-    \includegraphics[height=0.42\linewidth]{56_4_expression}&
-    \includegraphics[height=0.42\linewidth]{23_2_expression}& 
-    \includegraphics[height=0.42\linewidth]{5_6_expression}\\[-0.8em]
-                    & \smaller b) Fits & 
-  \end{tabular}
-  \vspace{0.2em}
-  \caption{The reconstruction (b) is robust against scans (a) with artifacts, noise, and holes.}
-  \label{fig:difficult}
-\end{figure}
-The search for the closest compatible point takes only points into account which
-have conforming normals, are closer than a threshold, and are not on or close
-to the border of the scan. This has the effect of removing many outliers.  The
-search is sped up by organizing the target scan in a space partitioning tree
-made up of spheres.
-The correspondences are then weighted with a robust function by their
-residual distance. The robust function is linear for distances smaller than
-$2$mm, behaves like $1/x$ between $2$mm and
-$20$mm, and is zero for a distance larger than $20$mm.
-Note, that it is necessary to balance robustness and regularization, as the
-right balance depends on the noise characteristic of the data. Suitable values
-were determined manually from a few scans of the GavabDB database and kept
-constant for all experiments as well on the GavaDB as on the UND database.  In
-step~\ref{step_fit} the 3DMM is fit to 3D-3D point correspondences.  This is
-done with a gauss-newton least squares optimization, using an analytic Jacobian
-and Gauss-Newton Hessian approximation.  Denote the correspondence points by
-$\MAT u=\Matrix{\VEC u_1, \dots, \VEC u_n}$ and the rows of the model which
-correspond to the $i$th vertex by subscript $i$, then we can write the cost
-function mimized in this step as
-\begin{align}
-  f(\MAT R, \VEC t, \VEC\alpha) &= \sum_i \normLR{\MAT R( \VEC\mu_i + \MAT M_i\VEC\alpha) + \VEC t - \VEC u_i}^2 + \lambda\normLR{\VEC\alpha}^2\qquad.\label{eqn:mincost}
-\end{align}
-%We make the norm dependent on the target normal by  using an orthonormal
-%covariance matrix $\MAT C_i$ per vertex, which makes the cost of deviation
-%along the normal higher than deviations inside the target surface. 
-%\begin{align}
-%  \MAT C_i &= \Matrix{ \VEC n_i^T\\ \nu \VEC a_i^T\\\nu\VEC b_i^T} & \VEC n_i &\bot \VEC a_i \bot \VEC b_i \bot \VEC n_i
-%\end{align}
-%where $\VEC n_i$ is the normal of the target correspondence and $\nu$ is an
-%anisotropy parameter. If we do not use the anisotropic distance measure (i.e.
-%$\MAT C_i=\MAT I$), then the cost function Equation~\ref{eqn:mincost} can be
-%minimized more efficiently by changing it to
-This can be minimized more efficiently by changing the direction of the rigid transform to
-\begin{align}
-  f(\MAT R, \VEC t, \VEC\alpha) &= \sum_i \normLR{ \VEC\mu_i + \MAT M_i\VEC\alpha + {\VEC t'} - {\MAT R'}\VEC u_i }^2 + \lambda\normLR{\VEC\alpha}^2\nonumber\\
-  {\VEC t'} &= \MAT R^{-1}\VEC t\qquad {\MAT R'} = \MAT R^{-1}\qquad.
-\end{align}
-because then the Jacobian consists of a large constant part and three columns
-which depend on the iteration.
-\begin{align}
-  F_i &= \VEC\mu_i + \MAT M_i\VEC\alpha + {\VEC t'} - {\MAT R'_{r_1,r_2,r_3}}\VEC u_i\\
-  \diffp{F_i}{\VEC\alpha} &= \MAT M_i\qquad
-  \diffp{F_i}{\VEC t'} = \MAT I_3\qquad
-  \diffp{F_i}{r_i} = \diffp{\MAT R'_{r_1,r_2,r_3}}{r_i}\VEC u_i\\
-  \MAT J &= \Matrix{\MAT J_c & | & \MAT J_d }\\
-  \MAT J_c &= \Matrix{\MAT M & \VEC 1 \otimes \MAT I_3\\ \MAT I & \MAT 0}\\
-  \MAT J_d &= \Matrix{(\MAT I \otimes \diffp{\MAT R'}{r_1})\MAT u^T & (\MAT I \otimes \diffp{\MAT R'}{r_2})\MAT u^T& (\MAT I \otimes \diffp{\MAT R'}{r_3})\MAT u^T\\\MAT 0 & \MAT 0 & \MAT 0}
-\end{align}
-Accordingly, the Hessian can be approximated as
-\begin{align}
-  \MAT H &= \Matrix{
-    \MAT J_c^T\MAT J_c & (\MAT J_c^T\MAT J_d)^T\\
-    \MAT J_c^T\MAT J_d & \MAT J_d^T\MAT J_d
-  }\qquad.
-\end{align}
-By precalculating the constant parts of the matrices we can remove most of the
-computation time, making step~\ref{step_fit} very fast.
-
-We initialize the registration by locating the tip of the nose with a
-heuristic, which assumes that the head is upright and looking into the camera.
-This initialization is good enough to for a fully automatic
-fit, as the fitting behaves like rigid ICP in the beginning, and rigid ICP is
-known to have a large basin of convergence.
-
-\section{Experiments}
-\begin{figure*}
-  \begin{tabular}{cc}
-    \scalebox{0.82}{\input{shrec_MNCG}} &
-    \scalebox{0.82}{\input{und_MNCG}}
-  \end{tabular}
-  \caption{For the expression dataset the retrieval rate is improved by
-  including the expression model, while for the neutral expression dataset the
-  performance does not decrease. Plotted is the mean normalized cumulative
-  gain, which is the number of retrieved correct answers divided by the number
-  of possible correct answers. Note also the different scales of the MNCG
-  curves for the two datasets. Our approach has a high accuracy on the
-  neutral (UND) dataset.}
-  \label{fig:mcg}
-\end{figure*}
-\begin{figure*}
-  \begin{tabular}{cc}
-    \scalebox{0.82}{\input{shrec_PR}} &
-    \scalebox{0.82}{\input{und_PR}}
-  \end{tabular}
-  \caption{Use of the expression model improves retrieval performance.
-  Plotted are precision and recall for different retrieval depths. The lower
-  precision of the UND database is due to the fact that some queries have no
-  correct answers. For the UND database we achieve total recall when querying
-  nine answers, while the maximal number of scans per individual is eight,
-  while for the GavabDB database the expression model gives a strong
-    improvement in recall rate but full recall can not be achieved.}
-  \label{fig:precision_expression}
-\end{figure*}
-
-\begin{figure*}
-  \begin{tabular}{cc}
-    \scalebox{0.82}{\input{shrec_FARFRR}} &
-    \scalebox{0.82}{\input{und_FARFRR}}
-  \end{tabular}
-  \caption{Impostor detection is reliable, as the minimum distance to a match
-  is smaller than the minimum distance to a nonmatch. Note the vast increase in
-  recognition performance with the expression model on the expression database,
-  and the fact that the recognition rate is not decreasing on the neutral
-  database, even though we added expression invariance. We can operate at $0$\%
-  false acceptance rate with less than $4$\% false rejection rate, or less than
-  $1$\%\ FAR with less than $1$\%\ FRR.}
-  \label{fig:impostor}
-\end{figure*}
-We evaluated the system on two databases with and without
-the expression model. We used the GavabDB~\cite{gavabdb} database and the
-UND~\cite{bowyer05:2d3d_recognition} database. For both databases, only the shape information was
-used. The GavabDB database contains 427 scans, with seven scans per ID, three
-neutral and four expressions. The expressions in this dataset varies
-considerably, including sticking out the tongue and strong facial distortions.
-Additionally it has strong artifacts due to facial hair, motion and the bad
-scanner quality. This dataset is typical for a non-cooperative environment.
-The UND database was used in the face recognition grand challenge~\cite{frvt06} and consists
-of 953 scans, with one to eight scans per ID. It is of better quality and
-contains only slight expression variations. It represents a cooperative
-scenario.
-
-The fitting was initialized by detecting the nose, and assuming that the face is
-upright and looking along the $z$-axis. To detect the nose we
-first removed the spike artifacts typical of range scanners by repeated
-min-filtering and removal of large triangles, then we detect the vertex with
-the smallest depth, which in its horizontal slice is sufficiently closer to the
-camera than the other pixels in that slice. For the UND dataset this gives us
-reliably a point on the tip or ridge of the nose. The heuristic worked for 939
-out of 953 Scans, in the remaining 16 scans we marked the nose manually. The
-GavabDB database has the scans already aligned and the tip of the nose is at
-the origin. We used this information for the GavabDB experiments.  The same
-regularisation parameters were used for all experiments, even though the
-GavabDB data is more noisy than the UND data. The parameters were set manually
-based on a few scans from the GavabDB Database. We used 100 principal identity
-components and 30 expression components for all experiments.
-
-In the experiments the distances between all scans were calculated, and we
-measured recognition and retrieval rates by treating every scan once as the
-probe and all other scans as the gallery. Both databases were used
-independently.
-
-\subsection{Retrieval Measures}
-We measure similarity between faces in parameter space as the angle between the
-face parameters in Mahalanobis space, which has proven to have high recognition
-rates~\cite{blanz03:face_rec}. The distance measure is
-\begin{align}
-  s(\VEC\alpha_1, \VEC\alpha_2) &= \arccos\left(\frac{\VEC\alpha_1^T\VEC\alpha_2}{\norm{\VEC\alpha_1}\norm{\VEC\alpha_2}}\right)\qquad.
-\end{align}
-We observed that the angular measure gives slightly larger recognition rates
-than the Mahalanobis distance. The Mahalanobis angle has the effect of
-regarding all caricatures of a face, which lie on a ray from the origin towards
-any identity, as the same identity. We also evaluated other measures, but found
-them to be consistently worse than the Mahalanobis angle.
-
-\subsection{Results}
-As expected, the two datasets behave differently because of the presence of
-expressions in the examples. 
-
-\subsubsection{UND}
-For the UND database we have good recognition rates with the neutral
-model. The mean cumulative normalized gain curve in
-Figure~\ref{fig:mcg} shows for varying retrieval depth the number of
-correctly retrieved scans divided by the maximal number of scans that could be
-retrieved at this level. From this it can be seen that the first match is
-always the correct match, if there is any match in the database. But for some
-probes no example is in the gallery. Therefore for face recognition we have to
-threshold the maximum allowed distance to be able to reject impostors. Varying
-the distance threshold leads to varying false acceptance rates (FAR) and false
-rejection rates (FRR), which are shown in Figure~\ref{fig:impostor}. Even
-though we have been tuning the model to the GavabDB dataset and not the UND
-dataset our recognition rates at any FAR rate are as good or better than the
-best results from the face recognition vendor test. This shows, that our basic
-face recognition method without expression modelling gives convincing results.
-Now we analyze how the expression modelling impacts recognition results on this
-expression-less database. If face and expression space are not orthogonal, then
-adding invariance towards expressions should make the recognition rates
-decrease. In fact, we observe that the recognition results are slightly lower,
-but only by a marginal amount, and still on par with the results from the face
-recognition vendor test. Let us now turn towards the expression database, where
-we expect to see an increase in recognition rate due to the expression model.
-
-\subsubsection{GavabDB}
-The recognition rates on the GavabDB without expression model are not quite as
-good as for the expression-less UND dataset, so here we hope to find some
-improvement by using expression normalization. And indeed, the closest point
-recognition rate with only the neutral model is 96.25\% which can be improved
-to 98.36\% by adding the expression model. Also the FAR/FRR values decrease
-considerably. The largest improvement can be seen  in retrieval performance,
-displayed in the precision recall curves in
-Figure~\ref{fig:precision_expression} and mean cumulative normalized gain
-curves in Figure~\ref{fig:mcg}. This is because there are multiple examples in
-the gallery, so finding a single match is relatively easy. But retrieving all
-examples from the database, even those with strong expressions, is only made
-possible by the expression model.
-
-%\emph{TODO: Try also $k$-NN, that should give 100\% recognition rate on the
-%GavabDB too.}
-
-\section{Speed}
-Though the method as presented operates at only approximately 40 seconds per
-query, it has the potential for speedup. It is possible to parallelize the
-closest point estimation and the optimisation, and more elaborate fitting
-algorithms including multiresolution schemes can be developed. The speed also
-depends on the number of vertices and components, for the results presented
-here 11000 vertices and 100 neutral plus 30 expression components were fitted.
-
-\section{Conclusion}
-We have used a 3D Morphable Model with a separating expression model to develop
-an expression-invariant face recognition algorithm. We have shown, that the
-system has excellent recognition rates on difficult expression data and data
-taken in a cooperative environment. The introduction of expression invariance
-did not incur a significant loss of precision on easier neutral data. The strong prior
-knowledge of the 3DMM allows robust handling of noisy data and allowed us to
-build a fully automatic face recognition system. We also introduced a relatively
-efficient fitting algorithm, which, as it has the potential for
-paralellisation, could be made even faster. 
-
-Note that, as we do establish correspondence between the model and the scans,
-it is trivial to add image based classification for datasets where a calibrated
-photo is available. This can be done by comparing the rectified textures,
-which should result in even higher recognition rates. It is also important to
-note that the expression normalization described here for range data can be
-applied equally well to other modalities, using any of the proposed 3DMM
-fitting algorithms.
-
-In the future we plan to include the additional texture cues and make the
-method faster, such that it is applicable in real world scenarios where a
-processing time of 40 seconds per probe is still a problem. Furthermore we
-would like to investigate more sophisticated fitting algorithms and a morphable
-model with a larger expression space.
-
-%\section*{Acknowledgement}
-%The authors wish to thank P.\ Paysan for the data
-%acquisition. This work was supported in part by a grant from Microsoft
-%Research and the Swiss National Science Foundation (200021-103814 and NCCR COME project 5005-66380).
-
-{\small
-\bibliographystyle{ieee}
-%%use following if all content of bibtex file should be shown
-%\nocite{*}
-\bibliography{shrec_08}
-}
-\end{document}
-
-

templates/baposter-template/examples/shrec/shrec_FARFRR.tex

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templates/baposter-template/examples/shrec/shrec_MNCG.tex

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templates/baposter-template/examples/shrec/shrec_PR.tex

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templates/baposter-template/examples/shrec/shrec_far_frr.pdf

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templates/baposter-template/examples/shrec/shrec_far_frr.tex

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-% GNUPLOT: LaTeX picture with Postscript
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-  \selectfont
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templates/baposter-template/examples/shrec/shrec_mncg.tex

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-% GNUPLOT: LaTeX picture with Postscript
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-      Package color not loaded in conjunction with
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-    }{Either use 'blacktext' in gnuplot or load the package
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-    \renewcommand\color[2][]{}%
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-  \providecommand\includegraphics[2][]{%
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-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
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-    \newif\ifGPblacktext
-    \GPblacktexttrue
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-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
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-    \gplbacktext
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templates/baposter-template/examples/shrec/shrec_pr.tex

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-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
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-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
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-      \put(5454,360){\makebox(0,0){\strut{} 100}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}Precision \%}}}%
-      \put(3186,90){\makebox(0,0){\strut{}Recall \%}}%
-      \put(3186,2250){\makebox(0,0){\strut{}\bf \normalsize GavabDB: Precision Recall}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(2754,873){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(2754,693){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{shrec_pr}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/und_FARFRR.tex

@@ -1,125 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktextfalse
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
-      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
-      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
-      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
-      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
-      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
-      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
-      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
-      \def\colorrgb#1{\color{black}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color{black}}%
-      \expandafter\def\csname LT1\endcsname{\color{black}}%
-      \expandafter\def\csname LT2\endcsname{\color{black}}%
-      \expandafter\def\csname LT3\endcsname{\color{black}}%
-      \expandafter\def\csname LT4\endcsname{\color{black}}%
-      \expandafter\def\csname LT5\endcsname{\color{black}}%
-      \expandafter\def\csname LT6\endcsname{\color{black}}%
-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(5760.00,2520.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(918,540){\makebox(0,0)[r]{\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(918,720){\makebox(0,0)[r]{\strut{} 0.01}}%
-      \csname LTb\endcsname%
-      \put(918,900){\makebox(0,0)[r]{\strut{} 0.02}}%
-      \csname LTb\endcsname%
-      \put(918,1080){\makebox(0,0)[r]{\strut{} 0.03}}%
-      \csname LTb\endcsname%
-      \put(918,1260){\makebox(0,0)[r]{\strut{} 0.04}}%
-      \csname LTb\endcsname%
-      \put(918,1440){\makebox(0,0)[r]{\strut{} 0.05}}%
-      \csname LTb\endcsname%
-      \put(918,1620){\makebox(0,0)[r]{\strut{} 0.06}}%
-      \csname LTb\endcsname%
-      \put(918,1800){\makebox(0,0)[r]{\strut{} 0.07}}%
-      \csname LTb\endcsname%
-      \put(918,1980){\makebox(0,0)[r]{\strut{} 0.08}}%
-      \csname LTb\endcsname%
-      \put(1026,360){\makebox(0,0){\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(1764,360){\makebox(0,0){\strut{} 0.005}}%
-      \csname LTb\endcsname%
-      \put(2502,360){\makebox(0,0){\strut{} 0.01}}%
-      \csname LTb\endcsname%
-      \put(3240,360){\makebox(0,0){\strut{} 0.015}}%
-      \csname LTb\endcsname%
-      \put(3978,360){\makebox(0,0){\strut{} 0.02}}%
-      \csname LTb\endcsname%
-      \put(4716,360){\makebox(0,0){\strut{} 0.025}}%
-      \csname LTb\endcsname%
-      \put(5454,360){\makebox(0,0){\strut{} 0.03}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}FRR}}}%
-      \put(3240,90){\makebox(0,0){\strut{}FAR}}%
-      \put(3240,2250){\makebox(0,0){\strut{}UND: Recognition Performance}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(4635,1827){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(4635,1647){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{und_FARFRR}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/und_MNCG.tex

@@ -1,127 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktextfalse
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
-      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
-      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
-      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
-      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
-      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
-      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
-      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
-      \def\colorrgb#1{\color{black}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color{black}}%
-      \expandafter\def\csname LT1\endcsname{\color{black}}%
-      \expandafter\def\csname LT2\endcsname{\color{black}}%
-      \expandafter\def\csname LT3\endcsname{\color{black}}%
-      \expandafter\def\csname LT4\endcsname{\color{black}}%
-      \expandafter\def\csname LT5\endcsname{\color{black}}%
-      \expandafter\def\csname LT6\endcsname{\color{black}}%
-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(5760.00,2520.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(1026,540){\makebox(0,0)[r]{\strut{} 0.99}}%
-      \csname LTb\endcsname%
-      \put(1026,828){\makebox(0,0)[r]{\strut{} 0.992}}%
-      \csname LTb\endcsname%
-      \put(1026,1116){\makebox(0,0)[r]{\strut{} 0.994}}%
-      \csname LTb\endcsname%
-      \put(1026,1404){\makebox(0,0)[r]{\strut{} 0.996}}%
-      \csname LTb\endcsname%
-      \put(1026,1692){\makebox(0,0)[r]{\strut{} 0.998}}%
-      \csname LTb\endcsname%
-      \put(1026,1980){\makebox(0,0)[r]{\strut{} 1}}%
-      \csname LTb\endcsname%
-      \put(1134,360){\makebox(0,0){\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(1566,360){\makebox(0,0){\strut{} 2}}%
-      \csname LTb\endcsname%
-      \put(1998,360){\makebox(0,0){\strut{} 4}}%
-      \csname LTb\endcsname%
-      \put(2430,360){\makebox(0,0){\strut{} 6}}%
-      \csname LTb\endcsname%
-      \put(2862,360){\makebox(0,0){\strut{} 8}}%
-      \csname LTb\endcsname%
-      \put(3294,360){\makebox(0,0){\strut{} 10}}%
-      \csname LTb\endcsname%
-      \put(3726,360){\makebox(0,0){\strut{} 12}}%
-      \csname LTb\endcsname%
-      \put(4158,360){\makebox(0,0){\strut{} 14}}%
-      \csname LTb\endcsname%
-      \put(4590,360){\makebox(0,0){\strut{} 16}}%
-      \csname LTb\endcsname%
-      \put(5022,360){\makebox(0,0){\strut{} 18}}%
-      \csname LTb\endcsname%
-      \put(5454,360){\makebox(0,0){\strut{} 20}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}MNCG}}}%
-      \put(3294,90){\makebox(0,0){\strut{}$@x$}}%
-      \put(3294,2250){\makebox(0,0){\strut{}UND: Mean Normalized Cumulative Gain}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(4635,873){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(4635,693){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{und_MNCG}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/und_PR.tex

@@ -1,117 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktextfalse
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
-      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
-      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
-      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
-      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
-      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
-      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
-      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
-      \def\colorrgb#1{\color{black}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color{black}}%
-      \expandafter\def\csname LT1\endcsname{\color{black}}%
-      \expandafter\def\csname LT2\endcsname{\color{black}}%
-      \expandafter\def\csname LT3\endcsname{\color{black}}%
-      \expandafter\def\csname LT4\endcsname{\color{black}}%
-      \expandafter\def\csname LT5\endcsname{\color{black}}%
-      \expandafter\def\csname LT6\endcsname{\color{black}}%
-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(5760.00,2520.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(810,540){\makebox(0,0)[r]{\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(810,828){\makebox(0,0)[r]{\strut{} 0.2}}%
-      \csname LTb\endcsname%
-      \put(810,1116){\makebox(0,0)[r]{\strut{} 0.4}}%
-      \csname LTb\endcsname%
-      \put(810,1404){\makebox(0,0)[r]{\strut{} 0.6}}%
-      \csname LTb\endcsname%
-      \put(810,1692){\makebox(0,0)[r]{\strut{} 0.8}}%
-      \csname LTb\endcsname%
-      \put(810,1980){\makebox(0,0)[r]{\strut{} 1}}%
-      \csname LTb\endcsname%
-      \put(918,360){\makebox(0,0){\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(1825,360){\makebox(0,0){\strut{} 0.2}}%
-      \csname LTb\endcsname%
-      \put(2732,360){\makebox(0,0){\strut{} 0.4}}%
-      \csname LTb\endcsname%
-      \put(3640,360){\makebox(0,0){\strut{} 0.6}}%
-      \csname LTb\endcsname%
-      \put(4547,360){\makebox(0,0){\strut{} 0.8}}%
-      \csname LTb\endcsname%
-      \put(5454,360){\makebox(0,0){\strut{} 1}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}Precision}}}%
-      \put(3186,90){\makebox(0,0){\strut{}Recall}}%
-      \put(3186,2250){\makebox(0,0){\strut{}UND: Precision Recall}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(2754,873){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(2754,693){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{und_PR}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/und_far_frr.tex

@@ -1,115 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktexttrue
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
-      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
-      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
-      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
-      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
-      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
-      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
-      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
-      \def\colorrgb#1{\color{black}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color{black}}%
-      \expandafter\def\csname LT1\endcsname{\color{black}}%
-      \expandafter\def\csname LT2\endcsname{\color{black}}%
-      \expandafter\def\csname LT3\endcsname{\color{black}}%
-      \expandafter\def\csname LT4\endcsname{\color{black}}%
-      \expandafter\def\csname LT5\endcsname{\color{black}}%
-      \expandafter\def\csname LT6\endcsname{\color{black}}%
-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(5760.00,2520.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(810,540){\makebox(0,0)[r]{\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(810,900){\makebox(0,0)[r]{\strut{} 0.5}}%
-      \csname LTb\endcsname%
-      \put(810,1260){\makebox(0,0)[r]{\strut{} 1}}%
-      \csname LTb\endcsname%
-      \put(810,1620){\makebox(0,0)[r]{\strut{} 1.5}}%
-      \csname LTb\endcsname%
-      \put(810,1980){\makebox(0,0)[r]{\strut{} 2}}%
-      \csname LTb\endcsname%
-      \put(918,360){\makebox(0,0){\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(1825,360){\makebox(0,0){\strut{} 0.2}}%
-      \csname LTb\endcsname%
-      \put(2732,360){\makebox(0,0){\strut{} 0.4}}%
-      \csname LTb\endcsname%
-      \put(3640,360){\makebox(0,0){\strut{} 0.6}}%
-      \csname LTb\endcsname%
-      \put(4547,360){\makebox(0,0){\strut{} 0.8}}%
-      \csname LTb\endcsname%
-      \put(5454,360){\makebox(0,0){\strut{} 1}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}FRR \%}}}%
-      \put(3186,90){\makebox(0,0){\strut{}FAR \%}}%
-      \put(3186,2250){\makebox(0,0){\strut{}\bf \normalsize UND: Recognition Performance}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(4635,1827){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(4635,1647){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{und_far_frr}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/und_mncg.tex

@@ -1,127 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktexttrue
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
-      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
-      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
-      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
-      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
-      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
-      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
-      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
-      \def\colorrgb#1{\color{black}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color{black}}%
-      \expandafter\def\csname LT1\endcsname{\color{black}}%
-      \expandafter\def\csname LT2\endcsname{\color{black}}%
-      \expandafter\def\csname LT3\endcsname{\color{black}}%
-      \expandafter\def\csname LT4\endcsname{\color{black}}%
-      \expandafter\def\csname LT5\endcsname{\color{black}}%
-      \expandafter\def\csname LT6\endcsname{\color{black}}%
-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(5760.00,2520.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(918,540){\makebox(0,0)[r]{\strut{} 99.5}}%
-      \csname LTb\endcsname%
-      \put(918,828){\makebox(0,0)[r]{\strut{} 99.6}}%
-      \csname LTb\endcsname%
-      \put(918,1116){\makebox(0,0)[r]{\strut{} 99.7}}%
-      \csname LTb\endcsname%
-      \put(918,1404){\makebox(0,0)[r]{\strut{} 99.8}}%
-      \csname LTb\endcsname%
-      \put(918,1692){\makebox(0,0)[r]{\strut{} 99.9}}%
-      \csname LTb\endcsname%
-      \put(918,1980){\makebox(0,0)[r]{\strut{} 100}}%
-      \csname LTb\endcsname%
-      \put(1026,360){\makebox(0,0){\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(1469,360){\makebox(0,0){\strut{} 2}}%
-      \csname LTb\endcsname%
-      \put(1912,360){\makebox(0,0){\strut{} 4}}%
-      \csname LTb\endcsname%
-      \put(2354,360){\makebox(0,0){\strut{} 6}}%
-      \csname LTb\endcsname%
-      \put(2797,360){\makebox(0,0){\strut{} 8}}%
-      \csname LTb\endcsname%
-      \put(3240,360){\makebox(0,0){\strut{} 10}}%
-      \csname LTb\endcsname%
-      \put(3683,360){\makebox(0,0){\strut{} 12}}%
-      \csname LTb\endcsname%
-      \put(4126,360){\makebox(0,0){\strut{} 14}}%
-      \csname LTb\endcsname%
-      \put(4568,360){\makebox(0,0){\strut{} 16}}%
-      \csname LTb\endcsname%
-      \put(5011,360){\makebox(0,0){\strut{} 18}}%
-      \csname LTb\endcsname%
-      \put(5454,360){\makebox(0,0){\strut{} 20}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}MNCG \%}}}%
-      \put(3240,90){\makebox(0,0){\strut{}$@(x)$}}%
-      \put(3240,2250){\makebox(0,0){\strut{}\bf \normalsize UND: Mean Normalized Cumulative Gain}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(4635,873){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(4635,693){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{und_mncg}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup

templates/baposter-template/examples/shrec/und_pr.tex

@@ -1,117 +0,0 @@
-% GNUPLOT: LaTeX picture with Postscript
-\begingroup
-  \fontfamily{phv}%
-  \selectfont
-  \makeatletter
-  \providecommand\color[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package color not loaded in conjunction with
-      terminal option `colourtext'%
-    }{See the gnuplot documentation for explanation.%
-    }{Either use 'blacktext' in gnuplot or load the package
-      color.sty in LaTeX.}%
-    \renewcommand\color[2][]{}%
-  }%
-  \providecommand\includegraphics[2][]{%
-    \GenericError{(gnuplot) \space\space\space\@spaces}{%
-      Package graphicx or graphics not loaded%
-    }{See the gnuplot documentation for explanation.%
-    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
-    \renewcommand\includegraphics[2][]{}%
-  }%
-  \providecommand\rotatebox[2]{#2}%
-  \@ifundefined{ifGPcolor}{%
-    \newif\ifGPcolor
-    \GPcolortrue
-  }{}%
-  \@ifundefined{ifGPblacktext}{%
-    \newif\ifGPblacktext
-    \GPblacktexttrue
-  }{}%
-  % define a \g@addto@macro without @ in the name:
-  \let\gplgaddtomacro\g@addto@macro
-  % define empty templates for all commands taking text:
-  \gdef\gplbacktext{}%
-  \gdef\gplfronttext{}%
-  \makeatother
-  \ifGPblacktext
-    % no textcolor at all
-    \def\colorrgb#1{}%
-    \def\colorgray#1{}%
-  \else
-    % gray or color?
-    \ifGPcolor
-      \def\colorrgb#1{\color[rgb]{#1}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
-      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
-      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
-      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
-      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
-      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
-      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
-      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
-      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
-    \else
-      % gray
-      \def\colorrgb#1{\color{black}}%
-      \def\colorgray#1{\color[gray]{#1}}%
-      \expandafter\def\csname LTw\endcsname{\color{white}}%
-      \expandafter\def\csname LTb\endcsname{\color{black}}%
-      \expandafter\def\csname LTa\endcsname{\color{black}}%
-      \expandafter\def\csname LT0\endcsname{\color{black}}%
-      \expandafter\def\csname LT1\endcsname{\color{black}}%
-      \expandafter\def\csname LT2\endcsname{\color{black}}%
-      \expandafter\def\csname LT3\endcsname{\color{black}}%
-      \expandafter\def\csname LT4\endcsname{\color{black}}%
-      \expandafter\def\csname LT5\endcsname{\color{black}}%
-      \expandafter\def\csname LT6\endcsname{\color{black}}%
-      \expandafter\def\csname LT7\endcsname{\color{black}}%
-      \expandafter\def\csname LT8\endcsname{\color{black}}%
-    \fi
-  \fi
-  \setlength{\unitlength}{0.0500bp}%
-  \begin{picture}(5760.00,2520.00)%
-    \gplgaddtomacro\gplbacktext{%
-      \csname LTb\endcsname%
-      \put(810,540){\makebox(0,0)[r]{\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(810,828){\makebox(0,0)[r]{\strut{} 20}}%
-      \csname LTb\endcsname%
-      \put(810,1116){\makebox(0,0)[r]{\strut{} 40}}%
-      \csname LTb\endcsname%
-      \put(810,1404){\makebox(0,0)[r]{\strut{} 60}}%
-      \csname LTb\endcsname%
-      \put(810,1692){\makebox(0,0)[r]{\strut{} 80}}%
-      \csname LTb\endcsname%
-      \put(810,1980){\makebox(0,0)[r]{\strut{} 100}}%
-      \csname LTb\endcsname%
-      \put(918,360){\makebox(0,0){\strut{} 0}}%
-      \csname LTb\endcsname%
-      \put(1825,360){\makebox(0,0){\strut{} 20}}%
-      \csname LTb\endcsname%
-      \put(2732,360){\makebox(0,0){\strut{} 40}}%
-      \csname LTb\endcsname%
-      \put(3640,360){\makebox(0,0){\strut{} 60}}%
-      \csname LTb\endcsname%
-      \put(4547,360){\makebox(0,0){\strut{} 80}}%
-      \csname LTb\endcsname%
-      \put(5454,360){\makebox(0,0){\strut{} 100}}%
-      \put(180,1260){\rotatebox{90}{\makebox(0,0){\strut{}Precision \%}}}%
-      \put(3186,90){\makebox(0,0){\strut{}Recall \%}}%
-      \put(3186,2250){\makebox(0,0){\strut{}\bf \normalsize UND: Precision Recall}}%
-    }%
-    \gplgaddtomacro\gplfronttext{%
-      \csname LTb\endcsname%
-      \put(2754,873){\makebox(0,0)[r]{\strut{}neutral model}}%
-      \csname LTb\endcsname%
-      \put(2754,693){\makebox(0,0)[r]{\strut{}expression model}}%
-    }%
-    \gplbacktext
-    \put(0,0){\includegraphics{und_pr}}%
-    \gplfronttext
-  \end{picture}%
-\endgroup