Minor corrections.

2 files changed, 22 insertions, 15 deletions

diff --git a/experimental.tex b/experimental.tex
index cfdaf78..982fcf7 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -1,4 +1,5 @@
 \section{Real-World Evaluation}
+\label{sec:experiment}
 
 We conduct a real-life uncontrolled experiment using the Kinect to test to the
 algorithm.  First we present the manner and environment in which we perform
@@ -35,7 +36,8 @@ the RGB camera.
   \begin{center}
     \includegraphics[width=0.99\textwidth]{graphics/hallway.png}
   \end{center}
-  \caption{}
+  \caption{Experiment setting. Color image, depth image, and fitted
+  skeleton as captured by the Kinect in a single frame}
   \label{fig:hallway}
 \end{figure}
 
@@ -77,6 +79,10 @@ with the same skeleton ID.
 
 \begin{table}
 \begin{center}
+\caption{Data set statistics. The right part of the table shows the
+average numbers for different intervals of $k$, the rank of a person
+in the ordering given by the number of frames}
+
 \begin{tabular}{|l|r||r|r|r|}
 \hline
 Number of people & 25 & $k\leq 5$ & $5\leq k\leq 20$ & $k\geq 20$\\
@@ -87,9 +93,6 @@ Number of runs & 244 & 18 & 8 & 4\\
 \hline
 \end{tabular}
 \end{center}
-\caption{Data set statistics. The right part of the table shows the
-average numbers for different intervals of $k$, the rank of a person
-in the ordering given by the number of frames.}
 \label{tab:dataset}
 \end{table}
 
diff --git a/intro.tex b/intro.tex
index 5a4c0c1..5e51a36 100644
--- a/intro.tex
+++ b/intro.tex
@@ -31,15 +31,19 @@ Xbox 360~\cite{} does use the height inferred from the Kinect as part of its
 user identification algorithm, albeit in addition to other attributes including
 face recognition.
 
-The paper is organized as follows.  First we discuss prior methods of person
-identification, in addition to the advances in the technologies pertaining to
-skeleton mapping (Section~\ref{sec:related}).  Next we use a dataset of actual
-skeletal measurements to show that identification by skeleton is feasible, even
-when we simulate the error of measuring skeletons with a Kinect
-(Section~\ref{sec:}).  Finally, we Lastly, we collect skeleton data with a
-Kinect in an uncontrolled setting and we apply preprocessing and classification
-algorithms to this dataset (Section~\ref{sec:}).  We evaluate the performance
-of skeleton recognition with varying group size and compare it to face
-recognition. We conclude by discussing challenges working with the Kinect and
-future work (Section~\ref{sec:conclusion}).
+The paper is organized as follows.  First we discuss prior methods of
+person identification, in addition to the advances in the technologies
+pertaining to skeleton mapping (Section~\ref{sec:related}).  Next we
+use a dataset of actual skeletal measurements to show that
+identification by skeleton is feasible, even when we simulate the
+error of measuring skeletons with a Kinect
+(Section~\ref{sec:uniqueness}). We then discuss an error model and the
+resulting algorithm to do person identification
+(Section~\ref{sec:algorithm}).  Finally, we collect skeleton data with
+a Kinect in an uncontrolled setting and we apply preprocessing and
+classification algorithms to this dataset
+(Section~\ref{sec:experiment}).  We evaluate the performance of
+skeleton recognition with varying group size and compare it to face
+recognition. We conclude by discussing challenges working with the
+Kinect and future work (Section~\ref{sec:conclusion}).