Merge pull request #473 from mshabunin:add_msd_detector

Author: alalek

modules/xfeatures2d/doc/xfeatures2d.bib

@@ -64,3 +64,10 @@ @article{Tola10
   volume = "32",
   number = "5"
 }
+
+@inproceedings{Tombari14,
+  title={Interest Points via Maximal Self-Dissimilarities},
+  author={Tombari, Federico and Di Stefano, Luigi},
+  booktitle={Asian Conference on Computer Vision -- ACCV 2014},
+  year={2014}
+}

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -159,7 +159,7 @@ LATCH is a binary descriptor based on learned comparisons of triplets of image p
 * half_ssd_size - the size of half of the mini-patches size. For example, if we would like to compare triplets of patches of size 7x7x
     then the half_ssd_size should be (7-1)/2 = 3.
 
-Note: the descriptor can be coupled with any keypoint extractor. The only demand is that if you use set rotationInvariance = True then 
+Note: the descriptor can be coupled with any keypoint extractor. The only demand is that if you use set rotationInvariance = True then
     you will have to use an extractor which estimates the patch orientation (in degrees). Examples for such extractors are ORB and SIFT.
 
 Note: a complete example can be found under /samples/cpp/tutorial_code/xfeatures2D/latch_match.cpp
@@ -258,6 +258,27 @@ class CV_EXPORTS_W DAISY : public Feature2D
 
 };
 
+/** @brief Class implementing the MSD (*Maximal Self-Dissimilarity*) keypoint detector, described in @cite 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.
+
+*/
+
+class CV_EXPORTS_W MSDDetector : public Feature2D {
+
+public:
+
+    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);
+};
 
 //! @}
 

modules/xfeatures2d/perf/perf_msd.cpp

@@ -0,0 +1,33 @@
+#include "perf_precomp.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace xfeatures2d;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+typedef perf::TestBaseWithParam<std::string> msd;
+
+#define MSD_IMAGES \
+    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "stitching/a3.png"
+
+PERF_TEST_P(msd, detect, testing::Values(MSD_IMAGES))
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+
+    if (frame.empty())
+        FAIL() << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame);
+    Ptr<MSDDetector> detector = MSDDetector::create();
+    vector<KeyPoint> points;
+
+    TEST_CYCLE() detector->detect(frame, points, mask);
+
+    sort(points.begin(), points.end(), comparators::KeypointGreater());
+    SANITY_CHECK_KEYPOINTS(points, 1e-3);
+}