<commit_before>\chapter{Feature Extraction}
\section{Introduction}
Under the term {\em Feature Extraction} we include several techniques
aiming to detect or extract informations of low level of abstraction
from images. These {\em features} can be objects : points, lines,
etc. They can also be measures : moments, textures, etc.


\section{Interest Points}
\subsection{Harris detector}
\input{HarrisExample}
\subsection{SIFT detector}
\input{SIFTExample}

\section{Alignments}
\label{sec:Alignments}
\input{AlignmentsExample}
\section{Lines}
\label{sec:LineDetectors}

\subsection{Line Detection}
\label{sec:LineDetection}
\input{RatioLineDetectorExample}
\input{CorrelationLineDetectorExample}
\input{AssymmetricFusionOfLineDetectorExample}<commit_msg>DOC: include SIFTFastExample<commit_after>\chapter{Feature Extraction}
\section{Introduction}
Under the term {\em Feature Extraction} we include several techniques
aiming to detect or extract informations of low level of abstraction
from images. These {\em features} can be objects : points, lines,
etc. They can also be measures : moments, textures, etc.


\section{Interest Points}
\subsection{Harris detector}
\input{HarrisExample}
\subsection{SIFT detector}
\input{SIFTExample}
\input{SIFTFastExample}

\section{Alignments}
\label{sec:Alignments}
\input{AlignmentsExample}
\section{Lines}
\label{sec:LineDetectors}

\subsection{Line Detection}
\label{sec:LineDetection}
\input{RatioLineDetectorExample}
\input{CorrelationLineDetectorExample}
\input{AssymmetricFusionOfLineDetectorExample}
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