Searched refs:Hamming (Results 1 – 10 of 10) sorted by relevance
/external/opencv3/modules/features2d/test/ |
D | test_descriptors_regression.cpp | 315 CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk", in TEST() 316 (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f, in TEST() 324 CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", in TEST() 326 … (CV_DescriptorExtractorTest<Hamming>::DistanceType)25.f, in TEST() 328 … (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f, in TEST() 344 CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze", in TEST() 345 … (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f, in TEST() 347 Hamming(), AKAZE::create()); in TEST()
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/external/opencv3/doc/py_tutorials/py_feature2d/py_brief/ |
D | py_brief.markdown | 22 strings are used to match features using Hamming distance. This provides better speed-up because 36 use Hamming Distance to match these descriptors.
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/external/opencv3/modules/core/include/opencv2/core/ |
D | base.hpp | 408 struct CV_EXPORTS Hamming struct 419 typedef Hamming HammingLUT;
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/external/opencv3/doc/tutorials/features2d/akaze_matching/ |
D | akaze_matching.markdown | 67 We use Hamming distance, because AKAZE uses binary descriptor by default.
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/external/opencv3/modules/flann/include/opencv2/ |
D | flann.hpp | 96 using ::cvflann::Hamming;
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/external/opencv3/doc/py_tutorials/py_feature2d/py_matcher/ |
D | py_matcher.markdown | 22 Hamming distance as measurement. If ORB is using WTA_K == 3 or 4, cv2.NORM_HAMMING2 should be
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/external/opencv3/modules/flann/include/opencv2/flann/ |
D | dist.h | 415 struct Hamming
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/external/opencv3/modules/flann/src/ |
D | miniflann.cpp | 332 typedef ::cvflann::Hamming<uchar> HammingDistance;
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/external/opencv3/modules/features2d/src/opencl/ |
D | brute_force_match.cl | 116 #elif (DIST_TYPE == 6) // Hamming
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/external/opencv3/modules/core/src/ |
D | stat.cpp | 2486 Hamming::ResultType Hamming::operator()( const unsigned char* a, const unsigned char* b, int size )… in operator ()()
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