Stochastic Hierarchical Watershed Cut Based on Disturbed Topographical Surface

In this article we present a hierarchical stochastic image segmentation approach. This approach is based on a framework of edge-weighted graph for minimum spanning forest hierarchy. Image regions, that are represented by trees in a forest, can be merged according to a certain rule in order to create...

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Bibliographic Details
Main Authors: Pimentel Filho, Carlos Alberto F., Araujo, Arnaldo de Albuquerque, Cousty, Jean, Guimaraes, Silvio Jamil F., Najman, Laurent
Format: Conference Proceeding
Language:English
Subjects:
hierarchical segmentation
Image edge detection
Image segmentation
mathematical morphology
Stochastic processes
stochastic segmentation
Surface morphology
Surface topography
Surface treatment
Vegetation
watershed
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Summary:In this article we present a hierarchical stochastic image segmentation approach. This approach is based on a framework of edge-weighted graph for minimum spanning forest hierarchy. Image regions, that are represented by trees in a forest, can be merged according to a certain rule in order to create a single tree that represents segments hierarchically. In this article, we propose to add a uniform random noise into the edge-weighted graph and then we build the hierarchy with several realizations of independent segmentations. At the end, we combine all the hierarchical segmentations into a single one. As we show in this article, adding noise into the edge weights improves the segmentation precision of larger image regions and for F-Measure of objects and parts.
ISSN:2377-5416
DOI:10.1109/SIBGRAPI.2016.044