131 lines
8.2 KiB
ReStructuredText
131 lines
8.2 KiB
ReStructuredText
Feature Detection and Description
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=================================
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.. highlight:: cpp
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FAST
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--------
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Detects corners using the FAST algorithm
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.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true, type=FastFeatureDetector::TYPE_9_16 )
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:param image: Image where keypoints (corners) are detected.
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:param keypoints: Keypoints detected on the image.
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:param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below.
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:param nonmaxSupression: If it is true, non-maximum suppression is applied to detected corners (keypoints).
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:param type: one of the three neighborhoods as defined in the paper: ``FastFeatureDetector::TYPE_9_16``, ``FastFeatureDetector::TYPE_7_12``, ``FastFeatureDetector::TYPE_5_8``
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Detects corners using the FAST algorithm by [Rosten06]_.
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.. [Rosten06] E. Rosten. Machine Learning for High-speed Corner Detection, 2006.
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MSER
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----
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.. ocv:class:: MSER : public FeatureDetector
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Maximally stable extremal region extractor. ::
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class MSER : public CvMSERParams
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{
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public:
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// default constructor
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MSER();
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// constructor that initializes all the algorithm parameters
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MSER( int _delta, int _min_area, int _max_area,
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float _max_variation, float _min_diversity,
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int _max_evolution, double _area_threshold,
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double _min_margin, int _edge_blur_size );
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// runs the extractor on the specified image; returns the MSERs,
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// each encoded as a contour (vector<Point>, see findContours)
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// the optional mask marks the area where MSERs are searched for
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void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
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};
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The class encapsulates all the parameters of the MSER extraction algorithm (see
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http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for useful comments and parameters description.
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ORB
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---
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.. ocv:class:: ORB : public Feature2D
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Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor, described in [RRKB11]_. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
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.. [RRKB11] Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to SIFT or SURF. ICCV 2011: 2564-2571.
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ORB::ORB
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--------
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The ORB constructor
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.. ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31)
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:param nfeatures: The maximum number of features to retain.
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:param scaleFactor: Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
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:param nlevels: The number of pyramid levels. The smallest level will have linear size equal to ``input_image_linear_size/pow(scaleFactor, nlevels)``.
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:param edgeThreshold: This is size of the border where the features are not detected. It should roughly match the ``patchSize`` parameter.
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:param firstLevel: It should be 0 in the current implementation.
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:param WTA_K: The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as ``NORM_HAMMING2`` (2 bits per bin). When ``WTA_K=4``, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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:param scoreType: The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to ``KeyPoint::score`` and is used to retain best ``nfeatures`` features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
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:param patchSize: size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
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ORB::operator()
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---------------
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Finds keypoints in an image and computes their descriptors
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.. ocv:function:: void ORB::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
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:param image: The input 8-bit grayscale image.
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:param mask: The operation mask.
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:param keypoints: The output vector of keypoints.
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:param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
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:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
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FREAK
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-----
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.. ocv:class:: FREAK : public DescriptorExtractor
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Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in [AOV12]_. The algorithm propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are competitive alternatives to existing keypoints in particular for embedded applications.
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.. [AOV12] A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award Winner.
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FREAK::FREAK
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------------
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The FREAK constructor
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.. ocv:function:: FREAK::FREAK( bool orientationNormalized=true, bool scaleNormalized=true, float patternScale=22.0f, int nOctaves=4, const vector<int>& selectedPairs=vector<int>() )
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:param orientationNormalized: Enable orientation normalization.
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:param scaleNormalized: Enable scale normalization.
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:param patternScale: Scaling of the description pattern.
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:param nOctaves: Number of octaves covered by the detected keypoints.
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:param selectedPairs: (Optional) user defined selected pairs indexes,
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FREAK::selectPairs
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------------------
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Select the 512 best description pair indexes from an input (grayscale) image set. FREAK is available with a set of pairs learned off-line. Researchers can run a training process to learn their own set of pair. For more details read section 4.2 in: A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012.
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We notice that for keypoint matching applications, image content has little effect on the selected pairs unless very specific what does matter is the detector type (blobs, corners,...) and the options used (scale/rotation invariance,...). Reduce corrThresh if not enough pairs are selected (43 points --> 903 possible pairs)
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.. ocv:function:: vector<int> FREAK::selectPairs(const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const double corrThresh = 0.7, bool verbose = true)
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:param images: Grayscale image input set.
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:param keypoints: Set of detected keypoints
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:param corrThresh: Correlation threshold.
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:param verbose: Prints pair selection informations.
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