cv.MSDDetector - mexopencv

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.

Superclasses	handle
Sealed	false
Construct on load	false

	addlistener	Add listener for event.
	clear	Clears the algorithm state
	delete	Destructor
	detect	Detects keypoints in an image or image set
	empty	Checks if detector object is empty
	eq	== (EQ) Test handle equality.
	findobj	Find objects matching specified conditions.
	findprop	Find property of MATLAB handle object.
	ge	>= (GE) Greater than or equal relation for handles.
	getDefaultName	Returns the algorithm string identifier
	gt	> (GT) Greater than relation for handles.
Sealed	isvalid	Test handle validity.
	le	<= (LE) Less than or equal relation for handles.
	listener	Add listener for event without binding the listener to the source object.
	load	Loads algorithm from a file or a string
	lt	< (LT) Less than relation for handles.
	ne	~= (NE) Not equal relation for handles.
	notify	Notify listeners of event.
	save	Saves the algorithm parameters to a file
	typeid	Name of the C++ type (RTTI)