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OpenCV
4.2.0
Open Source Computer Vision
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OpenCV
4.2.0
Open Source Computer Vision
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Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [227]. More...
#include <opencv2/xfeatures2d.hpp>
Static Public Member Functions | |
| static Ptr< MSDDetector > | create (int m_patch_radius=3, int m_search_area_radius=5, int m_nms_radius=5, int m_nms_scale_radius=0, float m_th_saliency=250.0f, int m_kNN=4, float m_scale_factor=1.25f, int m_n_scales=-1, bool m_compute_orientation=false) |
 Static Public Member Functions inherited from cv::Algorithm | |
| template<typename _Tp > | |
| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file. More... | |
| template<typename _Tp > | |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String. More... | |
| template<typename _Tp > | |
| static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node. More... | |
Additional Inherited Members | |
| Public Member Functions inherited from cv::Feature2D | |
| virtual | ~Feature2D () |
| virtual void | compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors) |
| Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). More... | |
| virtual void | compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors) |
| virtual int | defaultNorm () const |
| virtual int | descriptorSize () const |
| virtual int | descriptorType () const |
| virtual void | detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray()) |
| Detects keypoints in an image (first variant) or image set (second variant). More... | |
| virtual void | detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray()) |
| virtual void | detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) |
| virtual bool | empty () const CV_OVERRIDE |
| Return true if detector object is empty. More... | |
| virtual String | getDefaultName () const CV_OVERRIDE |
| void | read (const String &fileName) |
| virtual void | read (const FileNode &) CV_OVERRIDE |
| Reads algorithm parameters from a file storage. More... | |
| void | write (const String &fileName) const |
| virtual void | write (FileStorage &) const CV_OVERRIDE |
| Stores algorithm parameters in a file storage. More... | |
| void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| Public Member Functions inherited from cv::Algorithm | |
| Algorithm () | |
| virtual | ~Algorithm () |
| virtual void | clear () |
| Clears the algorithm state. More... | |
| virtual void | save (const String &filename) const |
| void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More... | |
| Protected Member Functions inherited from cv::Algorithm | |
| void | writeFormat (FileStorage &fs) const |
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [227].
The algorithm implements a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of "contextual self-dissimilarity" reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, it extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy.
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1.8.13