cv.SuperpixelSLIC - mexopencv

Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm

As described in [Achanta2012].

SLIC (Simple Linear Iterative Clustering) clusters pixels using pixel channels and image plane space to efficiently generate compact, nearly uniform superpixels. The simplicity of approach makes it extremely easy to use a lone parameter specifies the number of superpixels and the efficiency of the algorithm makes it very practical.

Several optimizations are available for SLIC class:

SLICO stands for "Zero parameter SLIC" and it is an optimization of baseline SLIC described in [Achanta2012].
MSLIC stands for "Manifold SLIC" and it is an optimization of baseline SLIC described in [Liu_2017_IEEE].

References

[Achanta2012]:

Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. "SLIC superpixels compared to state-of-the-art superpixel methods". IEEE Trans. Pattern Anal. Mach. Intell., 34(11):2274-2282, nov 2012.

[epfl2010]:

"SLIC Superpixels" Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. EPFL Technical Report no. 149300, June 2010.

[Liu_2017_IEEE]:

Yong-Jin Liu, Cheng-Chi Yu, Min-Jing Yu, and Ying He. "Intrinsic Manifold SLIC: A Simple and Efficient Method for Computing Content-Sensitive Superpixels". IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 99, March 2017.

Superclasses	handle
Sealed	false
Construct on load	false

	addlistener	Add listener for event.
	clear	Clears the algorithm state
	delete	Destructor
	empty	Checks if detector object is empty
	enforceLabelConnectivity	Enforce label connectivity
	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
	getLabelContourMask	Returns the mask of the superpixel segmentation stored in object
	getLabels	Returns the segmentation labeling of the image
	getNumberOfSuperpixels	Calculates the actual amount of superpixels on a given segmentation computed and stored in object
	gt	> (GT) Greater than relation for handles.
Sealed	isvalid	Test handle validity.
	iterate	Calculates the superpixel segmentation on a given image with the initialized parameters in the object
	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