Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector.

Maximal Self-Dissimilarity Interest Point Detector, as 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.

References:

[Tombari14]:

Federico Tombari and Luigi Di Stefano. "Interest Points via Maximal Self-Dissimilarities". In Asian Conference on Computer Vision, ACCV 2014.