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void | setPatchRadius (int patch_radius) |
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int | getPatchRadius () |
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void | setSearchAreaRadius (int use_orientation) |
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int | getSearchAreaRadius () |
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void | setNmsRadius (int nms_radius) |
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int | getNmsRadius () |
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void | setNmsScaleRadius (int nms_scale_radius) |
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int | getNmsScaleRadius () |
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void | setThSaliency (float th_saliency) |
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float | getThSaliency () |
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void | setKNN (int kNN) |
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int | getKNN () |
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void | setScaleFactor (float scale_factor) |
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float | getScaleFactor () |
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void | setNScales (int use_orientation) |
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int | getNScales () |
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void | setComputeOrientation (bool compute_orientation) |
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bool | getComputeOrientation () |
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override string | getDefaultName () |
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void | detect (Mat image, MatOfKeyPoint keypoints, Mat mask) |
| Detects keypoints in an image (first variant) or image set (second variant). More...
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void | detect (Mat image, MatOfKeyPoint keypoints) |
| Detects keypoints in an image (first variant) or image set (second variant). More...
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void | detect (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > masks) |
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void | detect (List< Mat > images, List< MatOfKeyPoint > keypoints) |
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void | compute (Mat image, MatOfKeyPoint keypoints, Mat descriptors) |
| Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). More...
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void | compute (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > descriptors) |
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void | detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors, bool useProvidedKeypoints) |
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void | detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors) |
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int | descriptorSize () |
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int | descriptorType () |
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int | defaultNorm () |
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void | write (string fileName) |
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void | read (string fileName) |
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override bool | empty () |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
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IntPtr | getNativeObjAddr () |
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virtual void | clear () |
| Clears the algorithm state. More...
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void | save (string filename) |
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void | Dispose () |
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void | ThrowIfDisposed () |
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static new MSDDetector | __fromPtr__ (IntPtr addr) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius, float m_th_saliency, int m_kNN, float m_scale_factor, int m_n_scales, bool m_compute_orientation) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius, float m_th_saliency, int m_kNN, float m_scale_factor, int m_n_scales) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius, float m_th_saliency, int m_kNN, float m_scale_factor) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius, float m_th_saliency, int m_kNN) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius, float m_th_saliency) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius, int m_nms_radius) |
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static MSDDetector | create (int m_patch_radius, int m_search_area_radius) |
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static MSDDetector | create (int m_patch_radius) |
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static MSDDetector | create () |
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static new Feature2D | __fromPtr__ (IntPtr addr) |
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static Algorithm | __fromPtr__ (IntPtr addr) |
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static IntPtr | ThrowIfNullIntPtr (IntPtr ptr) |
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Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [Tombari14].
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.