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