Added the defaultNorm() method to the DescriptorExtractor class. This method returns the default norm type for each descriptor type. The tests and C/C++ samples were updated to get the norm type directly from the DescriptorExtractor inherited classes.
This was reported in feature report #2182 (http://code.opencv.org/issues/2182). It will make it possible to get the norm type usually applied matching method for each descriptor, instead of passing it manually.
Original pull requests:
#996 from jet47:gpu-nvcuvid-libraries
#995 from jet47:fix-bug-2985
#999 from snosov1:unreliable-results-fix
#1005 from alekcac:doc_fix
#1004 from jet47:fix-bug-3068
#987 from jet47:bug-3085-fix
#969 from pengx17:2.4_binary_cache
#929 from dominikrose:mingw-libdc1394-2-windows
#1000 from ivan-korolev:fix_sift_bug_2892
#1001 from ivan-korolev:fix_stitching_bug_2405
#998 from asmorkalov:android_cmake_mips_fix
#993 from ivan-korolev:fix_videostab_bug_3023
#988 from snosov1:3071-fix
#986 from pengx17:2.4_initiated_context
#982 from pengx17:2.4_fix_two_bugs
#981 from SeninAndrew:ximea_camera_support_fix
#991 from asmorkalov:android_javadoc_fix
#972 from jet47:mog2-params-bug-2168
#980 from SpecLad:include-config
#973 from pengx17:2.4_oclclahe
#903 from aks2:2.4
#968 from asmorkalov:android_na_cproj_fix
#971 from SpecLad:matchers-ctor
#970 from asmorkalov:dshow_valid_check_fix
#965 from apavlenko:fix_java_empty_mats
Conflicts:
cmake/OpenCVModule.cmake
modules/core/src/matmul.cpp
modules/gpu/CMakeLists.txt
modules/ocl/include/opencv2/ocl/ocl.hpp
modules/ocl/perf/perf_imgproc.cpp
modules/ocl/src/imgproc.cpp
modules/ocl/src/initialization.cpp
modules/stitching/src/matchers.cpp
modules/video/src/video_init.cpp
modules/videostab/src/global_motion.cpp
modified SIFT to 1) double image before finding keypoints, 2) use floating-point internally instead of 16-bit integers, 3) set the keypoint response to the abs(interpolated_DoG_value). step 1) increases the number of detected keypoints significantly and together with 2) and 3) it improves some detection benchmarks. On the other hand, the stability of the small keypoints is lower, so the rotation and scale invariance tests now struggle a bit. In 2.5 need to make this feature optional and add some more intelligence to the algorithm.
added test that finds a planar object using SIFT.
