| /external/opencv3/doc/py_tutorials/py_imgproc/py_pyramids/ |
| D | py_pyramids.markdown | 23 There are two kinds of Image Pyramids. 1) Gaussian Pyramid and 2) Laplacian Pyramids
25 Higher level (Low resolution) in a Gaussian Pyramid is formed by removing consecutive rows and
30 Similarly while expanding, area becomes 4 times in each level. We can find Gaussian pyramids using
50 Laplacian Pyramids are formed from the Gaussian Pyramids. There is no exclusive function for that.
53 Gaussian Pyramid and expanded version of its upper level in Gaussian Pyramid. The three levels of a
73 2. Find the Gaussian Pyramids for apple and orange (in this particular example, number of levels
75 3. From Gaussian Pyramids, find their Laplacian Pyramids
88 # generate Gaussian pyramid for A
95 # generate Gaussian pyramid for B
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| /external/opencv3/doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/ |
| D | gausian_median_blur_bilateral_filter.markdown | 52 ### Gaussian Filter
54 - Probably the most useful filter (although not the fastest). Gaussian filtering is done by
55 convolving each point in the input array with a *Gaussian kernel* and then summing them all to
57 - Just to make the picture clearer, remember how a 1D Gaussian kernel look like?
66 Remember that a 2D Gaussian can be represented as :
82 - In an analogous way as the Gaussian filter, the bilateral filter also considers the neighboring
84 which is the same weighting used by the Gaussian filter. The second component takes into account
140 /// Applying Gaussian blur
141 if( display_caption( "Gaussian Blur" ) != 0 ) { return 0; }
213 -# **Gaussian Filter:**
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_filtering/ |
| D | py_filtering.markdown | 86 ### 2. Gaussian Blurring
92 zeros, they are calculated from kernel size. Gaussian blurring is highly effective in removing
95 If you want, you can create a Gaussian kernel with the function, **cv2.getGaussianKernel()**.
97 The above code can be modified for Gaussian blurring:
132 function of pixel difference. Gaussian function of space make sure only nearby pixels are considered
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| /external/opencv3/doc/tutorials/imgproc/pyramids/ |
| D | pyramids.markdown | 31 - **Gaussian pyramid:** Used to downsample images
34 - In this tutorial we'll use the *Gaussian pyramid*.
36 #### Gaussian Pyramid
44 - To produce layer \f$(i+1)\f$ in the Gaussian pyramid, we do the following:
45 - Convolve \f$G_{i}\f$ with a Gaussian kernel:
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| /external/webrtc/webrtc/base/ |
| D | random.h | 48 double Gaussian(double mean, double standard_deviation);
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| D | random.cc | 69 double Random::Gaussian(double mean, double standard_deviation) { in Gaussian() function in webrtc::Random
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| D | random_unittest.cc | 270 TEST(RandomNumberGeneratorTest, Gaussian) { in TEST() argument
280 int index = prng.Gaussian(kMean, kStddev) + 0.5; in TEST()
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| /external/opencv3/doc/py_tutorials/py_video/py_bg_subtraction/ |
| D | py_bg_subtraction.markdown | 32 It is a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It was introduced in
35 pixel by a mixture of K Gaussian distributions (K = 3 to 5). The weights of the mixture represent
70 It is also a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It is based on two
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| /external/opencv3/doc/py_tutorials/py_feature2d/py_sift_intro/ |
| D | py_sift_intro.markdown | 35 For this, scale-space filtering is used. In it, Laplacian of Gaussian is found for the image with
44 approximation of LoG. Difference of Gaussian is obtained as the difference of Gaussian blurring of
46 different octaves of the image in Gaussian Pyramid. It is represented in below image:
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| /external/opencv3/doc/tutorials/imgproc/imgtrans/laplace_operator/ |
| D | laplace_operator.markdown | 49 - Remove noise by applying a Gaussian blur and then convert the original image to grayscale
76 -# Apply a Gaussian blur to reduce noise:
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_thresholding/ |
| D | py_thresholding.markdown | 100 'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
149 # Otsu's thresholding after Gaussian filtering
159 'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
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| /external/webrtc/webrtc/modules/remote_bitrate_estimator/ |
| D | overuse_detector_unittest.cc | 62 random_.Gaussian(0, standard_deviation_ms) + 0.5)); in Run100000Samples()
85 random_.Gaussian(0, standard_deviation_ms) + 0.5)); in RunUntilOveruse()
127 int index = random_.Gaussian(49, 10); in TEST_F()
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| /external/opencv3/doc/tutorials/imgproc/imgtrans/canny_detector/ |
| D | canny_detector.markdown | 23 -# Filter out any noise. The Gaussian filter is used for this purpose. An example of a Gaussian
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| /external/opencv3/doc/py_tutorials/py_feature2d/py_surf_intro/ |
| D | py_surf_intro.markdown | 19 In SIFT, Lowe approximated Laplacian of Gaussian with Difference of Gaussian for finding
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| /external/opencv3/doc/tutorials/core/interoperability_with_OpenCV_1/ |
| D | interoperability_with_OpenCV_1.markdown | 16 - How to add some Gaussian noise to an image
108 image some Gaussian noise and then mix together the channels according to some formula.
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| /external/opencv3/doc/tutorials/imgproc/imgtrans/hough_circle/ |
| D | hough_circle.markdown | 61 -# Apply a Gaussian blur to reduce noise and avoid false circle detection:
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| /external/opencv3/modules/ml/doc/ |
| D | ml_intro.markdown | 50 function is assumed to be a Gaussian mixture, one component per class. Using the training data the
303 density function in the form of a Gaussian mixture distribution with a specified number of mixtures.
306 space drawn from a Gaussian mixture:
352 Estimation for Gaussian Mixture and Hidden Markov Models_. Technical Report TR-97-021,
392 - Gaussian function ( cv::ml::ANN_MLP::GAUSSIAN ): \f$f(x)=\beta e^{-\alpha x*x}\f$ , which is not
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_canny/ |
| D | py_canny.markdown | 21 image with a 5x5 Gaussian filter. We have already seen this in previous chapters.
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| /external/opencv3/doc/py_tutorials/py_photo/py_non_local_means/ |
| D | py_non_local_means.markdown | 16 In earlier chapters, we have seen many image smoothing techniques like Gaussian Blurring, Median
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| /external/opencv3/doc/tutorials/imgproc/imgtrans/sobel_derivatives/ |
| D | sobel_derivatives.markdown | 49 -# The Sobel Operator combines Gaussian smoothing and differentiation.
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_grabcut/ |
| D | py_grabcut.markdown | 42 - Now a Gaussian Mixture Model(GMM) is used to model the foreground and background.
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| /external/webrtc/webrtc/modules/remote_bitrate_estimator/test/ |
| D | bwe_test_framework.cc | 397 int64_t gaussian_random = random->Gaussian(mean, std_dev); in TruncatedNSigmaGaussian()
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| /external/opencv3/doc/tutorials/highgui/video-input-psnr-ssim/ |
| D | video_input_psnr_ssim.markdown | 234 one, where one corresponds to perfect fit. Unfortunately, the many Gaussian blurring is quite
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/ |
| D | py_fourier_transform.markdown | 110 Better option is Gaussian Windows.
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| /external/opencv3/doc/ |
| D | opencv.bib | 802 title = {Improved adaptive Gaussian mixture model for background subtraction},
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