Searched refs:hough (Results 1 – 11 of 11) sorted by relevance
| /external/opencv3/samples/gpu/ |
| D | houghlines.cpp | 62 …Ptr<cuda::HoughSegmentDetector> hough = cuda::createHoughSegmentDetector(1.0f, (float) (CV_PI / 18… in main() local
64 hough->detect(d_src, d_lines); in main()
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| /external/opencv3/modules/cudaimgproc/perf/ |
| D | perf_hough.cpp | 107 …cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, thres… in PERF_TEST_P() local
109 TEST_CYCLE() hough->detect(d_src, d_lines); in PERF_TEST_P()
156 …cv::Ptr<cv::cuda::HoughSegmentDetector> hough = cv::cuda::createHoughSegmentDetector(rho, theta, m… variable
158 TEST_CYCLE() hough->detect(d_mask, d_lines);
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| /external/opencv3/modules/cudaimgproc/src/ |
| D | hough_segments.cpp | 56 namespace hough namespace
136 using namespace cv::cuda::device::hough; in detect()
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| D | hough_lines.cpp | 56 namespace hough namespace
133 using namespace cv::cuda::device::hough; in detect()
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| D | hough_circles.cpp | 56 namespace hough namespace
162 using namespace cv::cuda::device::hough; in detect()
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_houghlines/ |
| D | py_houghlines.markdown | 52 Courtesy: [Amos Storkey](http://homepages.inf.ed.ac.uk/amos/hough.html) )
56 This is how hough transform for lines works. It is simple, and may be you can implement it using
68 applying hough transform. Second and third parameters are \f$\rho\f$ and \f$\theta\f$ accuracies
103 In the hough transform, you can see that even for a line with two arguments, it takes a lot of
107 Hough Transform and Probabilistic Hough Transform in hough space. (Image Courtesy : [Franck
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| /external/opencv3/modules/cudaimgproc/test/ |
| D | test_hough.cpp | 97 …cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, thres… in CUDA_TEST_P() local
100 hough->detect(loadMat(src, useRoi), d_lines); in CUDA_TEST_P()
103 hough->downloadResults(d_lines, lines); in CUDA_TEST_P()
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| /external/opencv3/modules/cudaimgproc/src/cuda/ |
| D | build_point_list.cu | 50 namespace hough namespace
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_houghcircles/ |
| D | py_houghcircles.markdown | 16 equation, we can see we have 3 parameters, so we need a 3D accumulator for hough transform, which
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| /external/opencv3/doc/ |
| D | opencv.bib | 489 title = {Robust detection of lines using the progressive probabilistic hough transform},
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| /external/opencv3/ |
| D | Android.mk | 724 modules/imgproc/src/hough.cpp \
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