Merge branch 'fg'

5 files changed, 56 insertions, 59 deletions

diff --git a/Makefile b/Makefile
index d5e8807..21a85d1 100644
--- a/Makefile
+++ b/Makefile
@@ -4,7 +4,7 @@ INPUTFILES  = $(shell grep "input{" $(DOCUMENT).tex | grep -v "^\%" | sed s/.\*i
 TEXFILES    = $(DOCUMENT).tex $(addsuffix .tex,$(INCLUDEFILES)) $(addsuffix .tex,$(INPUTFILES))
 BIBTEX 	    = $(shell grep "bibliography{" $(DOCUMENT).tex | grep -v "^\%" | sed s/.\*bibliography\{\\\(.*\\\)\}.\*/\\\1/ | sed s/,/\ /  )
 BIBTEXFILES = $(addsuffix .bib,$(BIBTEX))
-FINALFN	    = kinect-eccv12
+FINALFN	    = kinect-fg13
 FIGFILES    = $(wildcard figs/*.pdf,figs/*.png,figs/*.jpeg,figs/*.jpg,figs/*.gif)
 
 #all: ps pdf
diff --git a/algorithm.tex b/algorithm.tex
index 3045929..c02a0e5 100644
--- a/algorithm.tex
+++ b/algorithm.tex
@@ -55,15 +55,15 @@ This suggests that all class conditionals $P(\bx | y)$ are multivariate normal
 and our generative model, although very simple, may be nearly optimal
 \cite{bishop06pattern}.
 
-\begin{figure}[t]
+\begin{figure*}[t]
   \centering
-  \includegraphics{graphics/limbs.pdf}
+  \includegraphics[width=0.99\textwidth]{graphics/limbs.pdf}
   \caption{Histograms of differences between 9 skeleton measurements
   $x_k$ (Section~\ref{sec:experiment}) and their expectation given the
   class $y$. In red, the p.d.f. of a normal distribution with mean and
   variance equal to the empirical mean and variance of the measurement}
   \label{fig:error marginals}
-\end{figure}
+\end{figure*}
 
 
 \subsection{Sequential hypothesis testing}
diff --git a/experimental.tex b/experimental.tex
index 909e4cb..fde1395 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -49,7 +49,7 @@ detected figures, and the fitted skeleton of a person in a single frame.  Skelet
 fit from roughly 1-5 meters away from the Kinect.  For each frame with a
 skeleton we record the color image and the positions of the joints.
 
-\begin{figure}[t]
+\begin{figure*}[t]
   \begin{center}
     \includegraphics[width=0.99\textwidth]{graphics/hallway.png}
   \end{center}
@@ -57,7 +57,7 @@ skeleton we record the color image and the positions of the joints.
   \caption{Experiment setting. Color image, depth image, and fitted
   skeleton as captured by the Kinect in a single frame}
   \label{fig:hallway}
-\end{figure}
+\end{figure*}
 
 For some frames, one or several joints are out of the frame or are occluded by
 another part of the body. In those cases, the coordinates of these joints are
@@ -87,7 +87,8 @@ pairs of joints:
 %Shoulder-Elbow, Elbow-Wrist, ShoulderCenter-Spine, Spine-HipCenter,
 %HipCenter-HipSide, HipSide-Knee, Knee-Ankle.  
 
-\begin{table}
+\vspace{\baselineskip}
+\begin{table}[!h]
 \begin{center}
 \vspace{-1.5\baselineskip}
 \begin{tabular}{lll}
@@ -98,6 +99,7 @@ Shoulder-Elbow & Spine-HipCenter & Knee-Ankle\\
 \vspace{-2.5\baselineskip}
 \end{center}
 \end{table}
+\vspace{1.5\baselineskip}
 
 Each detected skeleton also has an ID number obtained from the figure detection
 stage. When there are consecutive frames with the same ID, it means that figure
@@ -142,7 +144,7 @@ happens if the noise from the Kinect is reduced.
 
 \begin{figure}[t]
   \begin{center}
-    \includegraphics[]{graphics/frames.pdf}
+    \includegraphics[width=0.49\textwidth]{graphics/frames.pdf}
   \end{center}
   \vspace{-1.5\baselineskip}
   \caption{Distribution of the frequency of each individual in the
@@ -190,11 +192,11 @@ we reach 90\% accuracy at 60\% recall for a group size of 10 people.
 \begin{figure*}[t]
 \begin{center}
 \subfloat[Mixture of Gaussians]{
-    \includegraphics[]{graphics/offline-nb.pdf}
+    \includegraphics[width=0.49\textwidth]{graphics/offline-nb.pdf}
     \label{fig:offline:nb}
 }
 \subfloat[Sequential Hypothesis Testing]{
-    \includegraphics[]{graphics/offline-sht.pdf}
+    \includegraphics[width=0.49\textwidth]{graphics/offline-sht.pdf}
     \label{fig:offline:sht}
 }
   \caption{Results with 10-fold cross-validation for the top $n_p$ most present people}
@@ -246,30 +248,22 @@ recognition rates mostly above 90\% for group sizes of 3 and 5.
 %    \label{fig:online:nb}
 %}
 %\subfloat[Sequential hypothesis testing]{
-\parbox[t]{0.49\linewidth}{
 \begin{center}
     \includegraphics[width=0.49\textwidth]{graphics/online-sht.pdf}
 \end{center}
-}
-\parbox[t]{0.49\linewidth}{
-  \begin{center}
-    \includegraphics[width=0.49\textwidth]{graphics/face.pdf}
-  \end{center}
-}
-\end{figure}
-\begin{figure}
 \vspace{-1.5\baselineskip}
-\parbox[t]{0.48\linewidth}{
     \caption{Results for the online setting, where $n_p$ is the size of
     the group as in Figure~\ref{fig:offline}}
     \label{fig:online}
-}
-\hspace{0.02\linewidth}
-\parbox[t]{0.48\linewidth}{
+\end{figure}
+\begin{figure}[t]
+  \begin{center}
+    \includegraphics[width=0.49\textwidth]{graphics/face.pdf}
+  \end{center}
+\vspace{-1.5\baselineskip}
   \caption{Results for face recognition versus skeleton recognition
   with $n_p=5$ people}
   \label{fig:face}
-}
 \end{figure}
 
 \subsection{Face recognition}
@@ -300,28 +294,20 @@ less than 10\% at most thresholds.
 
 
 \begin{figure}[t]
-\parbox[t]{0.49\linewidth}{
 \begin{center}
   \includegraphics[width=0.49\textwidth]{graphics/back.pdf}
 \end{center}
-}
-\parbox[t]{0.49\linewidth}{
+\vspace{-1.5\baselineskip}
+\caption{Results with people walking away from and toward the camera}
+\label{fig:back}
+\end{figure}
+\begin{figure}[t]
 \begin{center}
   \includegraphics[width=0.49\textwidth]{graphics/var.pdf}
 \end{center}
-}
-\end{figure}
-\begin{figure}
 \vspace{-1.5\baselineskip}
-\parbox[t]{0.48\linewidth}{
-\caption{Results with people walking away from and toward the camera}
-\label{fig:back}
-}
-\hspace{0.02\linewidth}
-\parbox[t]{0.48\linewidth}{
 \caption{Results with and without halving the variance of the noise}
 \label{fig:var}
-}
 \end{figure}
 
 \subsection{Walking away}
diff --git a/kinect.tex b/kinect.tex
index dd023b8..f684205 100644
--- a/kinect.tex
+++ b/kinect.tex
@@ -1,10 +1,11 @@
-\documentclass[runningheads]{llncs}
+%\documentclass[runningheads]{llncs}
+\documentclass[letterpaper, 10 pt, conference]{ieeeconf}
 
 \usepackage[dvips]{color}
 \usepackage{graphicx}
 \usepackage{amsmath,amssymb} % define this before the line numbering.
 \usepackage{color}
-\usepackage[width=122mm,left=12mm,paperwidth=146mm,height=193mm,top=12mm,paperheight=217mm]{geometry}
+%\usepackage[width=122mm,left=12mm,paperwidth=146mm,height=193mm,top=12mm,paperheight=217mm]{geometry}
 \usepackage{tabularx}
 \usepackage{url}
 \usepackage{xspace}
@@ -33,28 +34,28 @@
 \input{utils}
 
 \begin{document}
-\pagestyle{headings}
-\mainmatter
-\def\ECCV12SubNumber{543}  % Insert your submission number here
+%\pagestyle{headings}
+%\mainmatter
+%\def\ECCV12SubNumber{543}  % Insert your submission number here
 
 \title{Skeleton Recognition: Viability and Performance} % Replace with your title
 
-\author{
-\parbox[t]{0.33\linewidth}{Jon Whiteaker}
-}
-\author{
-Jon Whiteaker
-\and
-Thibaut Horel
-\and
-Branislav Kveton
-}
-\institute{
-Technicolor, Palo Alto, CA 94301, USA \\
-\email{\footnotesize jbw@berkeley.edu},\\
-\email{\footnotesize thibaut.horel@ens.fr},\\
-\email{\footnotesize branislav.kveton@technicolor.com} 
-}
+%\author{
+%\parbox[t]{0.33\linewidth}{Jon Whiteaker}
+%}
+%\author{
+%Jon Whiteaker
+%\and
+%Thibaut Horel
+%\and
+%Branislav Kveton
+%}
+%\institute{
+%Technicolor, Palo Alto, CA 94301, USA \\
+%\email{\footnotesize jbw@berkeley.edu},\\
+%\email{\footnotesize thibaut.horel@ens.fr},\\
+%\email{\footnotesize branislav.kveton@technicolor.com} 
+%}
 
 % \titlerunning{ECCV-12 submission ID \ECCV12SubNumber}
 % 
@@ -63,6 +64,16 @@ Technicolor, Palo Alto, CA 94301, USA \\
 % \author{Anonymous ECCV submission}
 % \institute{Paper ID \ECCV12SubNumber}
 
+\author{ \parbox{2 in}{\centering Jon Whiteaker\\
+         {\tt\small jbw@berkeley.edu}}
+         \parbox{2 in}{ \centering Thibaut Horel\\
+         {\tt\small thibaut.horel@ens.fr}}
+         \parbox{2 in}{ \centering Branislav Kveton\\
+         {\tt\small branislav.kveton@technicolor.com}}
+\\\\
+\centering\small Technicolor, Palo Alto, CA 94301, USA
+}
+
 
 \maketitle
 
diff --git a/uniqueness.tex b/uniqueness.tex
index 1814446..3d91dc8 100644
--- a/uniqueness.tex
+++ b/uniqueness.tex
@@ -68,7 +68,7 @@ output of the algorithm for the threshold $\delta$ is defined as:
 
 \begin{figure}[t]
   \begin{center}
-    \includegraphics[]{graphics/roc.pdf}
+    \includegraphics[width=0.49\textwidth]{graphics/roc.pdf}
   \end{center}
   \vspace{-1.5\baselineskip}
   \caption{ROC curve for several standard deviations of the noise and