Small corrections

1 files changed, 8 insertions, 8 deletions

diff --git a/uniqueness.tex b/uniqueness.tex
index 8f0b369..6c90310 100644
--- a/uniqueness.tex
+++ b/uniqueness.tex
@@ -52,7 +52,7 @@ each measurement of the pairs.
 \subsection{Results}
 
 We evaluate the performance of the pair-matching problem on the
-dataset by using a proximity threshold algorithm: for a given
+dataset by using a nearest neighbor algorithm: for a given
 threshold, a pair will be classified as \emph{matched} if the
 Euclidean distance between the two skeletons is lower than the
 threshold, and \emph{unmatched} otherwise. Formally, let
@@ -77,18 +77,18 @@ output of the algorithm for the threshold $\delta$ is defined as:
   \label{fig:roc}
 \end{figure}
 
-Figure \ref{fig:roc} shows the ROC curve of the proximity threshold
+Figure \ref{fig:roc} shows the ROC curve of the nearest neighbor
 algorithm for different values of the standard deviation of the noise,
 as well as the ROC of the best performing face detection algorithm in
 the image-restricted LFW benchmark: \emph{Associate-Predict}
 \cite{associate}.
 
-The results show that with a standard deviation of 3mm, skeleton
-proximity thresholding performs quite similarly to face detection at
-low false-positive rate. At this noise level, the error is smaller
-than 1cm with 99.9\% probability. Even with a standard deviation of
-5mm, it is still possible to detect 90\% of the matched pairs with a
-false positive rate of 6\%.
+The results show that with a standard deviation of 3mm, nearest
+neighbor performs quite similarly to face detection at low
+false-positive rate. At this noise level, the error is smaller than
+1cm with 99.9\% probability. Even with a standard deviation of 5mm, it
+is still possible to detect 90\% of the matched pairs with a false
+positive rate of 6\%.
 
 This experiment gives an idea of the noise variance level above which
 it is not possible to consistently distinguish skeletons. If the noise