Purpose: updated the feature2d chapter
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Elena Fedotova
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@ -1,4 +1,4 @@
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Feature detection and description
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Feature Detection and Description
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=================================
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.. highlight:: cpp
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@ -9,16 +9,15 @@ FAST
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--------
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.. c:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
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Detects corners using FAST algorithm by E. Rosten (''Machine learning for high-speed corner detection'', 2006).
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Detects corners using the FAST algorithm by E. Rosten (*Machine learning for high-speed corner detection*, 2006).
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:param image: The image. Keypoints (corners) will be detected on this.
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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 center pixel and
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pixels on circle around this pixel. See description of the algorithm.
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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 then non-maximum supression will be applied to detected corners (keypoints).
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:param nonmaxSupression: If it is true, non-maximum supression is applied to detected corners (keypoints).
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.. index:: MSER
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@ -28,7 +27,7 @@ MSER
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----
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.. c:type:: MSER
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Maximally-Stable Extremal Region Extractor ::
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Maximally (or Most??) stable extremal region extractor ::
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class MSER : public CvMSERParams
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{
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@ -46,8 +45,8 @@ Maximally-Stable Extremal Region Extractor ::
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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 MSER (see
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http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions) extraction algorithm.
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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).
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.. index:: StarDetector
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@ -57,14 +56,14 @@ StarDetector
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------------
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.. c:type:: StarDetector
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Implements Star keypoint detector ::
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Class implementing the Star keypoint detector ::
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class StarDetector : CvStarDetectorParams
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{
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public:
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// default constructor
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StarDetector();
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// the full constructor initialized all the algorithm parameters:
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// the full constructor that initializes all the algorithm parameters:
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// maxSize - maximum size of the features. The following
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// values of the parameter are supported:
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// 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
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@ -75,7 +74,7 @@ Implements Star keypoint detector ::
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// eliminate edges
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// lineThresholdBinarized - another threshold for the feature
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// size to eliminate edges.
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// The larger the 2 threshold, the more points you get.
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// The larger the 2nd threshold, the more points you get.
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StarDetector(int maxSize, int responseThreshold,
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int lineThresholdProjected,
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int lineThresholdBinarized,
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@ -85,8 +84,8 @@ Implements Star keypoint detector ::
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void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
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};
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The class implements a modified version of CenSurE keypoint detector described in
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Agrawal08
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The class implements a modified version of the CenSurE keypoint detector described in
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Agrawal08??.
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.. index:: SIFT
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@ -96,7 +95,7 @@ SIFT
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----
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.. c:type:: SIFT
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Class for extracting keypoints and computing descriptors using approach named Scale Invariant Feature Transform (SIFT). ::
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Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach ::
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class CV_EXPORTS SIFT
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{
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@ -160,7 +159,7 @@ Class for extracting keypoints and computing descriptors using approach named Sc
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//! returns the descriptor size in floats (128)
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int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
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//! finds the keypoints using SIFT algorithm
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//! finds the keypoints using the SIFT algorithm
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints) const;
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//! finds the keypoints and computes descriptors for them using SIFT algorithm.
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@ -186,7 +185,7 @@ SURF
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----
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.. c:type:: SURF
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Class for extracting Speeded Up Robust Features from an image. ::
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Class for extracting Speeded Up Robust Features from an image ::
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class SURF : public CvSURFParams
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{
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@ -211,10 +210,10 @@ Class for extracting Speeded Up Robust Features from an image. ::
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bool useProvidedKeypoints=false) const;
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};
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The class ``SURF`` implements Speeded Up Robust Features descriptor Bay06.
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There is fast multi-scale Hessian keypoint detector that can be used to find the keypoints
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(which is the default option), but the descriptors can be also computed for the user-specified keypoints.
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The function can be used for object tracking and localization, image stitching etc. See the ``find_obj.cpp`` demo in OpenCV samples directory.
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The class implements the Speeded Up Robust Features descriptor Bay06.
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There is a fast multi-scale Hessian keypoint detector that can be used to find keypoints
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(which is the default option). But the descriptors can be also computed for the user-specified keypoints.
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The function?? can be used for object tracking and localization, image stitching, and so on. See the ``find_obj.cpp`` demo in OpenCV samples directory.
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.. index:: RandomizedTree
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@ -224,7 +223,7 @@ RandomizedTree
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--------------
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.. c:type:: RandomizedTree
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The class contains base structure for ``RTreeClassifier`` ::
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Class containing a base structure for ``RTreeClassifier`` ::
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class CV_EXPORTS RandomizedTree
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{
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@ -241,7 +240,7 @@ The class contains base structure for ``RTreeClassifier`` ::
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RNG &rng, PatchGenerator &make_patch, int depth,
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int views, size_t reduced_num_dim, int num_quant_bits);
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// following two funcs are EXPERIMENTAL
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// next two functions are EXPERIMENTAL
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//(do not use unless you know exactly what you do)
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static void quantizeVector(float *vec, int dim, int N, float bnds[2],
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int clamp_mode=0);
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@ -271,8 +270,8 @@ The class contains base structure for ``RTreeClassifier`` ::
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int depth_;
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int num_leaves_;
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std::vector<RTreeNode> nodes_;
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float **posteriors_; // 16-bytes aligned posteriors
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uchar **posteriors2_; // 16-bytes aligned posteriors
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float **posteriors_; // 16-byte aligned posteriors
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uchar **posteriors2_; // 16-byte aligned posteriors
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std::vector<int> leaf_counts_;
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void createNodes(int num_nodes, RNG &rng);
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@ -298,17 +297,17 @@ RandomizedTree::train
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-------------------------
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.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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Trains a randomized tree using input set of keypoints
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Trains a randomized tree using an input set of keypoints.
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.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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{Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training}
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{Random numbers generator is used for training}
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{Patch generator is used for training}
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{Maximum tree depth}
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{Vector of ``BaseKeypoint`` type. Contains keypoints from the image that are used for training}??
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{Random numbers generator is used for training}??
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{Patch generator is used for training}??
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{Maximum tree depth}??
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{Number of dimensions are used in compressed signature}
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{Number of bits are used for quantization}
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{Number of dimensions are used in compressed signature}??
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{Number of bits are used for quantization}??
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.. index:: RandomizedTree::read
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@ -316,15 +315,15 @@ RandomizedTree::read
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------------------------
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.. c:function:: read(const char* file_name, int num_quant_bits)
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Reads pre-saved randomized tree from file or stream
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Reads a pre-saved randomized tree from a file or stream. ?? is it applied to the 1st func only?
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.. c:function:: read(std::istream \&is, int num_quant_bits)
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:param file_name: Filename of file contains randomized tree data
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:param file_name: Name of the file that contains randomized tree data.
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:param is: Input stream associated with file contains randomized tree data
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:param is: Input stream associated with the file that contains randomized tree data.
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{Number of bits are used for quantization}
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{Number of bits are used for quantization}??
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.. index:: RandomizedTree::write
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@ -332,13 +331,13 @@ RandomizedTree::write
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-------------------------
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.. c:function:: void write(const char* file_name) const
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Writes current randomized tree to a file or stream
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Writes the current randomized tree to a file or stream.
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.. c:function:: void write(std::ostream \&os) const
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:param file_name: Filename of file where randomized tree data will be stored
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:param file_name: Name of the file where randomized tree data is stored.
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:param is: Output stream associated with file where randomized tree data will be stored
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:param is: Output stream associated with the file where randomized tree data is stored.
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.. index:: RandomizedTree::applyQuantization
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@ -346,9 +345,9 @@ RandomizedTree::applyQuantization
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-------------------------------------
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.. c:function:: void applyQuantization(int num_quant_bits)
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Applies quantization to the current randomized tree
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Applies quantization to the current randomized tree.
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{Number of bits are used for quantization}
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{Number of bits are used for quantization}??
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.. index:: RTreeNode
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@ -358,7 +357,7 @@ RTreeNode
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---------
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.. c:type:: RTreeNode
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The class contains base structure for ``RandomizedTree`` ::
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Class containing a base structure for ``RandomizedTree`` ::
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struct RTreeNode
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{
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@ -386,7 +385,7 @@ RTreeClassifier
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---------------
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.. c:type:: RTreeClassifier
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The class contains ``RTreeClassifier`` . It represents calonder descriptor which was originally introduced by Michael Calonder ::
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Class containing ``RTreeClassifier`` . It represents the Calonder descriptor that was originally introduced by Michael Calonder ::
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class CV_EXPORTS RTreeClassifier
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{
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@ -456,19 +455,19 @@ RTreeClassifier::train
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--------------------------
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.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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Trains a randomized tree classificator using input set of keypoints
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Trains a randomized tree classifier using an input set of keypoints.
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.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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{Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training}
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{Random numbers generator is used for training}
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{Patch generator is used for training}
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{Number of randomized trees used in RTreeClassificator}
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{Maximum tree depth}
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{Vector of ``BaseKeypoint`` type. Contains image keypoints used for training}??
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{Random-number generator is used for training}??
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{Patch generator is used for training}??
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{Number of randomized trees used in RTreeClassificator}??
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{Maximum tree depth}??
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{Number of dimensions are used in compressed signature}
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{Number of bits are used for quantization}
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{Print current status of training on the console}
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{Number of dimensions are used in compressed signature}??
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{Number of bits are used for quantization}??
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{Print current status of training on the console}??
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.. index:: RTreeClassifier::getSignature
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@ -476,7 +475,7 @@ RTreeClassifier::getSignature
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---------------------------------
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.. c:function:: void getSignature(IplImage *patch, uchar *sig)
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Returns signature for image patch
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Returns a signature for an image patch.
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.. c:function:: void getSignature(IplImage *patch, float *sig)
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@ -490,11 +489,11 @@ RTreeClassifier::getSparseSignature
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.. c:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
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The function is simular to getSignaturebut uses the threshold for removing all signature elements less than the threshold. So that the signature is compressed
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Similarly to ``getSignaturebut`` , uses a threshold for removing all signature elements below the threshold so that the signature is compressed.??
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{Image patch to calculate signature for}
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{Output signature (array dimension is ``reduced_num_dim)``}
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{The threshold that is used for compressing the signature}
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{Image patch to calculate signature for}??
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{Output signature (array dimension is ``reduced_num_dim)``}??
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{The threshold that is used for compressing the signature}??
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.. index:: RTreeClassifier::countNonZeroElements
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@ -502,13 +501,13 @@ RTreeClassifier::countNonZeroElements
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-----------------------------------------
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.. c:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
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The function returns the number of non-zero elements in the input array.
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Returns the number of non-zero elements in an input array.
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:param vec: Input vector contains float elements
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:param vec: Input vector containing float elements.
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:param n: Input vector size
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:param n: Input vector size.
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{The threshold used for elements counting. We take all elements are less than ``tol`` as zero elements}
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{The threshold used for counting elements. We take all elements are less than ``tol`` as zero elements}??
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.. index:: RTreeClassifier::read
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@ -516,13 +515,13 @@ RTreeClassifier::read
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-------------------------
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.. c:function:: read(const char* file_name)
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Reads pre-saved RTreeClassifier from file or stream
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Reads a pre-saved ``RTreeClassifier`` from a file or stream.
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.. c:function:: read(std::istream& is)
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:param file_name: Filename of file contains randomized tree data
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:param file_name: Name of the file that contains randomized tree data.
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:param is: Input stream associated with file contains randomized tree data
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:param is: Input stream associated with the file that contains randomized tree data.
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.. index:: RTreeClassifier::write
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@ -530,13 +529,13 @@ RTreeClassifier::write
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--------------------------
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.. c:function:: void write(const char* file_name) const
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Writes current RTreeClassifier to a file or stream
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Writes the current RTreeClassifier to a file or stream.
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.. c:function:: void write(std::ostream \&os) const
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:param file_name: Filename of file where randomized tree data will be stored
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:param file_name: Name of the file where randomized tree data is stored.
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:param is: Output stream associated with file where randomized tree data will be stored
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:param is: Output stream associated with the file where randomized tree data is stored.
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.. index:: RTreeClassifier::setQuantization
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@ -544,13 +543,13 @@ RTreeClassifier::setQuantization
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------------------------------------
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.. c:function:: void setQuantization(int num_quant_bits)
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Applies quantization to the current randomized tree
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Applies quantization to the current randomized tree.
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{Number of bits are used for quantization}
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{Number of bits are used for quantization}??
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Below there is an example of ``RTreeClassifier`` usage for feature matching. There are test and train images and we extract features from both with SURF. Output is
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The example below demonstrates the usage of ``RTreeClassifier`` for feature matching. There are test and train images and features are extracted from both with SURF. Output is
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:math:`best\_corr` and
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:math:`best\_corr\_idx` arrays which keep the best probabilities and corresponding features indexes for every train feature. ::
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:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. ::
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CvMemStorage* storage = cvCreateMemStorage(0);
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CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
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