Thresholding (image processing): Difference between revisions

Revision as of 22:39, 3 April 2020

Revision as of 20:21, 15 April 2020

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* '''[[Histogram]] shape'''-based methods, where, for example, the peaks, valleys and curvatures of the smoothed histogram are analyzed

* '''Clustering'''-based methods, where the gray-level samples are clustered in two parts as background and foreground (object), or alternately are modeled as a mixture of two Gaussians

* '''[[Entropy (information theory)|Entropy]]'''-based methods result in algorithms that use the entropy of the foreground and background regions, the cross-entropy between the original and binarized image, etc.<ref>{{cite journal|last1=Zhang|first1=Y.|title=Optimal multi-level Thresholding based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach|journal=Entropy|date=2011|volume=13|issue=4|pages=841–859|doi=10.3390/e13040841|bibcode=2011Entrp..13..841Z|doi-access=free}}</ref>

* '''Object Attribute'''-based methods search a measure of similarity between the gray-level and the binarized images, such as fuzzy shape similarity, edge coincidence, etc.

* '''Spatial''' methods [that] use higher-order probability distribution and/or correlation between pixels