文件列表:

LICENSE (18026, 2017-05-29)
foveatedHessianDetector.h (21529, 2017-05-29)
foveatedTracking.h (5290, 2017-05-29)
history (9774, 2017-05-29)
linearFoveation.h (2608, 2017-05-29)
modelVisualization (0, 2017-05-29)
modelVisualization\figuraModel2D (0, 2017-05-29)
modelVisualization\figuraModel2D\Makefile (78, 2017-05-29)
modelVisualization\figuraModel2D\generateEpsFoveatedImageModel.cpp (2758, 2017-05-29)
modelVisualization\figuraModel2D\generateModel (12920, 2017-05-29)
modelVisualization\figuraModel2D\output.ps (199627, 2017-05-29)
modelVisualization\figuraModel2D\script.sh (214, 2017-05-29)
modelVisualization\figuraModel3D (0, 2017-05-29)
modelVisualization\figuraModel3D\Makefile (83, 2017-05-29)
modelVisualization\figuraModel3D\example.sh (224, 2017-05-29)
modelVisualization\figuraModel3D\generateEpsFoveatedPointCloudModel.cpp (3828, 2017-05-29)
modelVisualization\figuraModel3D\generateModel (12928, 2017-05-29)
modelVisualization\figuraModel3D\output.ps (302764, 2017-05-29)
test (0, 2017-05-29)
test\Makefile (1631, 2017-05-29)
test\extract.cpp (2663, 2017-05-29)
test\fovea1.yml (192, 2017-05-29)
test\fovea2.yml (210, 2017-05-29)
test\match.cpp (1794, 2017-05-29)
test\natal.jpg (117927, 2017-05-29)
test\scene_l.bmp (111670, 2017-05-29)
test\scene_r.bmp (111670, 2017-05-29)
test\tracking.cpp (2501, 2017-05-29)

foveatedFeatures ================ This is the source code of the Foveated Features Extraction, as a result of research done by Rafael Beserra. It uses Opencv2.4.8 for implementation and no more others dependencies. Consider citing the related paper to this project: Rafael Beserra Gomes, Bruno Motta de Carvalho, Luiz Marcos Garcia Gon§alves, Visual attention guided features selection with foveated images, Neurocomputing, Volume 120, 23 November 2013, Pages 34-44, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2012.10.033. This software is under GNU GPL (read LICENSE file for more informations). Foveation variable summary ---------------- The feature extraction process is done by a sequence of n steps. This differs slight from the original paper. If you want a easy configuration, use one of these values: - numberOfLevels = 4, etavector = [4, 3, 2, 1], levels = [0, 1, 2, 3], b = [1, 1, 1, 1] - numberOfLevels = 5, etavector = [5, 4, 3, 2, 1], levels = [0, 1, 2, 3, 4], b = [1, 1, 1, 1, 1] In the original paper, there are a B vector and a eta vector, each one with numberOfLevels elements. For example, 4 levels, B = {1, 1, 0, 1} and eta = {4, 3, 2, 1}, this set means that there are 4 steps for feature extraction: - first level (largest one) computes features in the fourth octave - second level computes features in the third octave - third level would compute features in the second octave, but is does not because B[3] = 0 - fourth level computes features in the first octave See section 3.2 from neurocomputing paper for examples. In this implementation, bvector (B), etavector (eta) and levelvector have n elements, the set {bvector[i], etavector[i], levelvector[i]} represents a feature extraction step. - bvector: [b1, b2, ..., bn]: a vector where bi is 0 if the feature extraction step number i should be discarded or 1, otherwise - etavector: [e1, e2, ..., en]: a vector where ei is the octave (> 0) for which the feature extraction step number i should be performed - levelvector: [l1, l2, ..., ln]: a vector where li is the foveated model level (>= 0 and < numberOfLevels) for which the feature extraction step number i should be performed For example: 4 levels, B = {1, 0, 1, 1, 1}, eta = {3, 4, 2, 3, 1} and levels = {0, 0, 1, 1, 3}, this set means that there are 5 steps for feature extraction: - first step (B[1] = 1, eta[1] = 3 and levels[1] = 0): a feature extraction in the third octave is performed in the first level (largest one) - second step (B[2] = 0, eta[2] = 4 and levels[2] = 0): a feature extraction in the fourth octave would be performed in the first level (largest one), but it does not because B[2] = 0 - third step (B[3] = 1, eta[3] = 2 and levels[3] = 1): a feature extraction in the second octave is performed in the second level - fourth step (B[4] = 1, eta[4] = 3 and levels[4] = 1): a feature extraction in the third octave is performed in the second level - fifth step (B[5] = 1, eta[5] = 1 and levels[5] = 3): a feature extraction in the first octave is performed in the fourth level This also means that: - first level (largest one) computes features in the third octave (levels[1] = 0, levels[2] = 0, eta[1] = 3, eta[2] = 4, but B[2] = 0) - second level computes features in the second and third octave (levels[3] = 1, levels[4] = 1, eta[3] = 2, eta[4] = 3) - third level has no feature extraction (since no level = 2) - fourth level computes features in the first octave (levels[5] = 3, eta[5] = 1) Usage ---------------- First, you must create a yml file that contains initial values of the foveated model. You MUST specify the following parameters: - smallestLevelWidth: 0 < value < imageWidth - smallestLevelHeight: 0 < value < imageHeight - numberOfLevels: value > 1 - bvector: [b1, b2, ..., bn], where each value is 0 or 1 - etavector: [e1, e2, ..., en], where each value > 0 - levelvector: [l1, l2, ..., ln], where value >= 0 and value < numberOfLevels Note that these last 3 sequence must have the same number of elements Second, use the struct FoveatedHessianDetectorParams to specify: - the image size (no default value) - the YML file path containing the foveated model parameters (no default file) - hessian threshold (default value: 100); - the number of layers in each octave (it is preferable to use default value: 3). Use FoveatedHessianDetectorParams(int imageWidth, int imageHeight, String ymlFile) construtor to specify the original image size and the yml file file path. After that, use foveatedHessianDetector function: static void foveatedHessianDetector(InputArray _img, InputArray _mask, vector& keypoints, FoveatedHessianDetectorParams params);

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