Article ID Journal Published Year Pages File Type
525712 Computer Vision and Image Understanding 2015 14 Pages PDF
Abstract

•We perform an unsupervised classification of the nodes of the Max-Tree.•The Max-Tree is considered as a hidden Markov tree.•Multivariate probability density functions enables to model multivariate attributes.•Model parameters are estimated from the sole observations.•We perform experiments on astronomical images and retinal images segmentation.

This article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, segmentation, and source detection, on astronomical and biomedical images. The obtained results show that the method is competitive despite its general formulation. This article provides also a new insight in the field of hierarchical Markovian image processing showing that morphological trees can advantageously replace traditional quadtrees.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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