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/external/opencv3/doc/py_tutorials/py_feature2d/py_sift_intro/
Dpy_sift_intro.markdown40 which gives us a list of \f$(x,y,\sigma)\f$ values which means there is a potential keypoint at (x,…
52 in previous scales. If it is a local extrema, it is a potential keypoint. It basically means that
53 keypoint is best represented in that scale. It is shown in below image:
73 If this ratio is greater than a threshold, called **edgeThreshold** in OpenCV, that keypoint is
81 Now an orientation is assigned to each keypoint to achieve invariance to image rotation. A
82 neigbourhood is taken around the keypoint location depending on the scale, and the gradient
85 with \f$\sigma\f$ equal to 1.5 times the scale of keypoint. The highest peak in the histogram is ta…
91 Now keypoint descriptor is created. A 16x16 neighbourhood around the keypoint is taken. It is
93 So a total of 128 bin values are available. It is represented as a vector to form keypoint
112 So now let's see SIFT functionalities available in OpenCV. Let's start with keypoint detection and
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/external/opencv3/modules/features2d/misc/java/test/
DSIFTDescriptorExtractorTest.java18 KeyPoint keypoint; field in SIFTDescriptorExtractorTest
34 …keypoint = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1); in setUp()
56 MatOfKeyPoint keypoints = new MatOfKeyPoint(keypoint); in testComputeMatListOfKeyPointMat()
/external/opencv3/doc/tutorials/features2d/akaze_matching/
Dakaze_matching.markdown69 -# **Use 2-nn matches to find correct keypoint matches**
104 If the distance from first keypoint's projection to the second keypoint is less than threshold,
/external/opencv3/doc/py_tutorials/py_feature2d/py_orb/
Dpy_orb.markdown19 ORB is basically a fusion of FAST keypoint detector and BRIEF descriptor with many modifications to
37 precomputed BRIEF patterns. As long as the keypoint orientation \f$\theta\f$ is consistent across v…
41 once it is oriented along keypoint direction, it loses this property and become more distributed.
/external/opencv3/doc/tutorials/introduction/transition_guide/
Dtransition_guide.markdown158 - cv::xfeatures2d::FREAK - Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor …
180 brief.compute(gray, query_kpts, query_desc); //Compute brief descriptors at each keypoint location
189 brief->compute(gray, query_kpts, query_desc); //Compute brief descriptors at each keypoint location
/external/opencv3/doc/py_tutorials/py_feature2d/py_surf_intro/
Dpy_surf_intro.markdown14 In last chapter, we saw SIFT for keypoint detection and description. But it was comparatively slow
43 keypoint where s is the size. It is divided into 4x4 subregions. For each subregion, horizontal and
/external/opencv3/modules/calib3d/src/
Dcirclesgrid.hpp153 cv::Point2f> &keypoint, std::vector<cv::Point2f> &warpedKeypoints);
/external/opencv3/doc/py_tutorials/py_feature2d/py_matcher/
Dpy_matcher.markdown38 will draw two match-lines for each keypoint. So we have to pass a mask if we want to selectively
/external/opencv3/
DAndroid.mk208 modules/features2d/src/keypoint.cpp \