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Merge remote-tracking branch 'origin/2.4' into merge-2.4

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Conflicts:
	CMakeLists.txt
	cmake/OpenCVDetectCUDA.cmake
	doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst
	modules/core/src/cmdparser.cpp
	modules/gpu/CMakeLists.txt
	modules/gpu/doc/introduction.rst
	modules/gpu/perf/perf_video.cpp
	modules/highgui/doc/reading_and_writing_images_and_video.rst
	modules/ocl/src/cl_context.cpp
	modules/video/include/opencv2/video/background_segm.hpp
	samples/cpp/image_sequence.cpp
	samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp
	samples/python/chessboard.py
	samples/python/cvutils.py
	samples/python/demhist.py
	samples/python/dft.py
	samples/python/distrans.py
	samples/python/edge.py
	samples/python/ffilldemo.py
	samples/python/fitellipse.py
	samples/python/houghlines.py
	samples/python/inpaint.py
	samples/python/logpolar.py
	samples/python/morphology.py
	samples/python/numpy_array.py
	samples/python/watershed.py
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Roman Donchenko
2013-12-03 15:50:44 +04:00
parent 17f53bc301 3ff1620086
commit d58cd9851f
125 changed files with 3283 additions and 304 deletions
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modules/objdetect/doc/cascade_classification.rst
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@@ -21,7 +21,7 @@ The word "cascade" in the classifier name means that the resultant classifier co
The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within the region of interest and the scale (this scale is not the same as the scale used at the detection stage, though these two scales are multiplied). For example, in the case of the third line feature (2c) the response is calculated as the difference between the sum of image pixels under the rectangle covering the whole feature (including the two white stripes and the black stripe in the middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the :ocv:func:`integral` description).
To see the object detector at work, have a look at the facedetect demo:
http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/dbt_face_detection.cpp
https://github.com/Itseez/opencv/tree/master/samples/cpp/dbt_face_detection.cpp
The following reference is for the detection part only. There is a separate application called ``opencv_traincascade`` that can train a cascade of boosted classifiers from a set of samples.