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Communication dans un congrès Année : 2017

Stochastic hierarchical watershed cut based on disturbed topographical surface

Résumé

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.
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Dates et versions

hal-01616394, version 1 (13-10-2017)

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Carols Alberto F. Pimentel Filho, Arnaldo Albuquerque de Araújo, Jean Cousty, Silvio Jamil F. Guimarães, Laurent Najman. Stochastic hierarchical watershed cut based on disturbed topographical surface. 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, Oct 2016, Sao Paulo, Brazil. ⟨10.1109/SIBGRAPI.2016.044⟩. ⟨hal-01616394⟩
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