report modified

Code/include/DetectionAndMatching/DetectionAndMatching.hpp

@@ -5,7 +5,15 @@
 /// \defgroup detectionandmatching Detection and Matching module
 ///
 /// Contains all the functions to compute the keypoints and
-/// match them on differents images
+/// match them on differents images. The algorithm used
+/// to detect the keypoints and compute the descriptors is BRISK. 
+/// The matches are computed with a brute force matcher provided 
+/// by OpenCV.
+///
+/// This program is meant to be used like this
+/// \code
+/// bin/DetectionAndMatching pathImg1 pathImg2 pathMask1 pathMask2
+/// \endcode
 /////////////////////////////////////////////////////////
 
 /////////////////////////////////////////////////////////

report/references.bib

@@ -1,6 +1,6 @@
 @article{brisk,
     author  = "Stefan, Leutenegger and Margarita, Chli and Roland, Y.Siegwart",
-    title   = "BRISK : Binary Robust Invariant Scalable Keypoints",
+    title   = "{BRISK} : Binary Robust Invariant Scalable Keypoints",
     year    = "2011",
 }
 
@@ -17,3 +17,10 @@
   url = "http://docs.opencv.org/trunk/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html",
   urldate = "2015-03-12"
 }
+
+@online{license,
+  author = "Open Source Initiative",
+  title = "The zlib/libpng License (Zlib)",
+  url = "http://opensource.org/licenses/Zlib",
+  urldate = "2015-03-12"
+}

report/report.tex

@@ -113,6 +113,7 @@
 \input{chapters/risksManagement}
 \input{chapters/actions}
 \input{chapters/schedule}
+\input{chapters/license}
 
 \input{chapters/conclusion}
 

report/subsections/pointsDetectionAndMatching.tex

@@ -1,41 +1,62 @@
 \subsection{Points detection and matching}
-\subsubsection{Points detection}
 \label{detection}
+\subsubsection{Points detection}
+
+As a first step the detection of keypoints was made by using SURF (Speeded-Up Robust Features) algorithm (provided by 
+OpenCV) because of its good 
+speed but this algorithm is computed in nonfree module which is not free to use in commercial 
+application. That is why we decided to choose the BRISK (Binary Robust Invariant Scalable Keypoints)
+algorithm (also provided by OpenCV) is pretty close to the SIFT (Scale-invariant feature transform) algorithm.
+Therefore the tests were made using SURF and BRISK algorithm but the final version is with BRISK as SURF is patented. 
+As the aera of interest in the images is the object, we provide a binary mask (computed during the segmentation) in input of
+SURF and BRISK detectors.
+
+
+SURF is a speed version of SIFT (see Figure \ref{siftSurf}). Indeed instead of approximate Laplacian of Gaussian (LoG) with Difference 
+of Gaussian for finding scale-space. SURF goes a little further and approximates LoG with Box Filter.
 
-  As a first step the detection of keypoints was made by using SURF (Speeded-Up Robust Features) algorithm (provided by 
-  OpenCV) because of its good 
-  speed but this algorithm is computed in nonfree module which is not free to use in commercial 
-  application. That is why we decided to choose the BRISK (Binary Robust Invariant Scalable Keypoints)
-  algorithm (also provided by OpenCV) is pretty close to the SIFT (Scale-invariant feature transform) algorithm.
-  Therefore the tests were made using SURF and BRISK algorithm but the final version is with BRISK as SURF is patented. 
-  As the aera of interest in the images is the object, we provide a binary mask (computed during the segmentation) in input of
-  SURF and B.
-  
-  SURF is a speed version of SIFT (see Figure \ref{siftSurf}). Indeed instead of approximate Laplacian of Gaussian (LoG) with Difference 
-  of Gaussian for finding scale-space. SURF goes a little further and approximates LoG with Box Filter.
-  
-  A detailed explanation of these algorithms can be found with the references \cite{brisk} (BRISK), \cite{surf} (SURF) and
-  \cite{sift} (SIFT).
-	
 \begin{figure}[H]
+  \centering
   \includegraphics[scale=0.45]{img/LapinSiftSurf}
   \caption{\label{siftSurf}Points detection for SIFT and SURF algorithm}
 \end{figure}
 
+For BRISK (see Figure \ref{brisk3415}) points of interest are identified across both the image and scale dimensions using a saliency criterion. 
+In order to increase the speed of computation, keypoints are detected in octave layers of the image pyramid as well as
+in layers in-between. The location and the scale of each keypoint are obtained in the continuous domain via quadratic function fitting.
+A sampling pattern consisting of points lying on appropriately scaled concentric circles is applied at the neighborhood of each keypoint to retrieve
+gray values. Finally, the oriented BRISK sampling pattern is used to obtain pairwise brightness comparison results which are 
+assembled into the binary BRISK descriptor. The BRISK constructor take in input three parameters which modify the results : 
+the thresh, the octave and the patternScale.
+
+
+A detailed explanation of these algorithms can be found with the references \cite{brisk} (BRISK), \cite{surf} (SURF) and
+\cite{sift} (SIFT).
+
+\begin{figure}[H]
+  \centering
+  \includegraphics[scale=0.45]{img/brisk3415}
+  \caption{\label{brisk3415}Detected points with BRISK algorithm}
+\end{figure}
+	
+
 \subsubsection{Points matching}
 
 We did the points matching, using a brute force matcher provided by OpenCV and then applied filters to get rid of inaccurate points.
+For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one.
 The filters used are :
 \begin{itemize}
   \item symmetric filter : the matches found when we take the image\_1 as base need to be found when we take the image\_2 as base also.
   \item order constraint : the position of each point is compared to each other in image\_1 and image\_2, if there is too much error these points
 			  are deleted.
-  \item threshold filter : filter on the distance between the descriptors of the matching points.
+  \item threshold filter : filter on the distance between the descriptors of the matching points. This filter is not used with BRISK 
+			    detection because the results are quite good without it.
   \item geometric filter : filter which use epipolar geometry, and the fundamental matrix to filter strange points.
 \end{itemize}
 
 
 \begin{figure}[H]
+  \centering
   \includegraphics[scale=0.45]{img/LapinSymetricGeometric}
   \caption{Points matching obtained after symmetric and geometric filtering}
 \end{figure}