From 2ec78535e079c799c03635b834bdfeafe0b4f6e6 Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Wed, 20 Mar 2013 23:29:41 -0700
Subject: did a pass on the paper, made some minor changes and did some cutting
 to get it under 8 pages

---
 intro.tex | 30 ++++++++++++++----------------
 1 file changed, 14 insertions(+), 16 deletions(-)

(limited to 'intro.tex')

diff --git a/intro.tex b/intro.tex
index 86bff8b..29be118 100644
--- a/intro.tex
+++ b/intro.tex
@@ -11,7 +11,7 @@ recognition.
 In recent years, advances in range cameras have given us access to increasingly
 accurate real-time depth imaging.  Furthermore, the low-cost and widely
 available Kinect~\cite{kinect} has brought range imaging to the masses.  In
-parallel, the automatic detection of body parts from depth images has led to
+parallel, automatic detection of body parts from depth images has led to
 real-time skeleton fitting.  
 
 %In this paper we show that skeleton fitting is accurate and unique enough in
@@ -19,10 +19,10 @@ real-time skeleton fitting.
 We make the following contributions.  First, we show that ground truth skeleton
 measurements can uniquely identify a person.  Second, we propose two models for
 skeleton recognition.  Finally, we evaluate our hypothesis using real-world
-data collected with the Kinect.  Our results show that skeleton recognition can
+data collected with a Kinect.  Our results show that skeleton recognition can
 identify three people with 95\% accuracy, and five people with 85\% accuracy.
-Furthermore, skeleton recognition can be performed in more situations than face
-recognition, such as when a person is not facing the camera.
+Furthermore, skeleton recognition can be performed in situations where face
+recognition cannot, such as when a person is not facing the camera.
 %As the resolution and accuracy of range cameras improve, so will the accuracy
 %and precision of skeleton fitting algorithms.  
 
@@ -33,16 +33,14 @@ recognition, such as when a person is not facing the camera.
 
 The paper is organized as follows.  First we discuss prior methods of
 person recognition, in addition to the advances in the technologies
-pertaining to skeleton fitting (Section~\ref{sec:related}).  Next we
-use a dataset of actual skeletal measurements to show that
-skeletons are a unique enough descriptor for person recognition.
-(Section~\ref{sec:uniqueness}). We then discuss an error model and the
-resulting algorithm to do person recognition
-(Section~\ref{sec:algorithms}).  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}).
+pertaining to skeleton fitting (Section~\ref{sec:related}).  Next we use a
+dataset of actual skeletal measurements to show that skeletons are a unique
+enough descriptor for person recognition.  (Section~\ref{sec:uniqueness}). We
+then discuss an error model and the resulting algorithm to do person
+recognition (Section~\ref{sec:algorithms}).  Finally, we collect skeleton data
+with a Kinect in an uncontrolled setting.  We apply preprocessing and
+classification algorithms to this dataset and evaluate the performance of
+skeleton recognition with varying group size (Section~\ref{sec:experiment}).
+We conclude by discussing challenges working with the Kinect and future work
+(Section~\ref{sec:conclusion}).
 
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