From 75bdb4858889f2af6e074ed9448b6ded1a81cbc4 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Mon, 5 Mar 2012 14:17:49 -0800
Subject: Small corrections

---
 experimental.tex | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'experimental.tex')

diff --git a/experimental.tex b/experimental.tex
index d03763b..8085321 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -47,7 +47,7 @@ outputs a set of joints in real world coordinates.  The view of the Kinect is
 seen in \fref{fig:hallway}, showing the color image, the depth image with
 figures, and the fitted skeleton of a person in a single frame.  Skeletons are
 fit from roughly 1-5 meters away from the Kinect.  For each frame with a
-skelton we record color image and the positions of the joints.
+skeleton we record color image and the positions of the joints.
 
 \begin{figure}[t]
   \begin{center}
@@ -232,14 +232,14 @@ building.
 %run. 
 We only evaluate SHT in this setting since it already takes consecutive frames
 into account and because it performed better than MoG in the offline setting
-(\ref{sec:experiment:offline}).  We partition the dataset into 10 time
+(\xref{sec:experiment:offline}).  We partition the dataset into 10 time
 sequences of equal size. For a given threshold, the algorithm is trained and
 tested incrementally: train on the first $k$ sequences (in the chronological
 order) and test on the $(k+1)$-th sequence. \fref{fig:online} shows the
 prediction-recall curve when averaging the prediction rate over the 10
 incremental experiments. Overall performance is worse than in
 \fref{fig:offline:sht} since the system trains on less data than in
-\ref{sec:experiment:offline} in all but the last step.  We still see
+\xref{sec:experiment:offline} in all but the last step.  We still see
 recognition rates mostly above 90\% for group sizes of 3 and 5.
 
 \begin{figure}[t]
-- 
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