From b262e999c17b651328a50fb303f08264fcd021e3 Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Fri, 2 Mar 2012 16:12:50 -0800
Subject: face file

---
 experimental.tex | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

(limited to 'experimental.tex')

diff --git a/experimental.tex b/experimental.tex
index 388e628..be1fc16 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -32,7 +32,7 @@ frame.  For each frame where a person is detected and a skeleton is fitted we
 collect the 3D coordinates of 20 body joints, and the color image recorded by
 the RGB camera.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.99\textwidth]{graphics/hallway.png}
   \end{center}
@@ -71,12 +71,11 @@ Head-ShoulderCenter, ShoulderCenter-Shoulder, Shoulder-Elbow, Elbow-Wrist,
 ShoulderCenter-Spine, Spine-HipCenter, HipCenter-HipSide, HipSide-Knee,
 Knee-Ankle.  Finally, any frame with a missing feature is filtered out.
 
-Each detected skeleton also has an ID number which identifies which figure
-it maps to from the figure detection stage. When there are consecutive number stays the
-same across several frames, it means that the skeleton-fitting
-algorithm was able to detect the skeleton in a contiguous way. This
-allows us to define the concept of a \emph{run}: a sequence of frames
-with the same skeleton ID.
+Each detected skeleton also has an ID number which identifies the figure it
+maps to from the figure detection stage. When there are consecutive frames with
+the same ID, it means that the skeleton-fitting algorithm was able to detect
+the skeleton in a contiguous way. This allows us to define the concept of a
+\emph{run}: a sequence of frames with the same skeleton ID.
 
 \begin{table}
 \begin{center}
@@ -120,7 +119,7 @@ number of runs available for the least present people, as seen in
 Table~\ref{tab:dataset}, which does not permit a proper training of
 the algorithm.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/10fold-naive.pdf}
   \end{center}
@@ -151,7 +150,7 @@ subsample. Figure~\ref{fig:sequential} shows the prediction-recall
 curve when averaging the prediction rate of the 10 incremental
 experiments.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/online-sht.pdf}
   \end{center}
@@ -195,7 +194,7 @@ classification and some of runs are dropped because it is not possible
 to recognize the person. Apart from that, the data set reduction is
 performed exactly as explained in Section~\ref{sec:experiment-design}.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/back.pdf}
   \end{center}
@@ -244,7 +243,7 @@ Figure~\ref{fig:var} compares the Precision-Recall curve of
 Figure~\ref{fig:sequential} to the curve of the same experiment run on
 the newly obtained data set.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/var.pdf}
   \end{center}
-- 
cgit v1.2.3-70-g09d2

