Stochastic hierarchical watershed cut based on disturbed topographical surface

Abstract : 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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Communication dans un congrès
29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, Oct 2016, Sao Paulo, Brazil. 2017, Graphics, Patterns and Images (SIBGRAPI), 2016 29th SIBGRAPI Conference on. 〈10.1109/SIBGRAPI.2016.044〉
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Soumis le : vendredi 13 octobre 2017 - 15:49:03
Dernière modification le : mardi 17 octobre 2017 - 01:04:03

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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. 2017, Graphics, Patterns and Images (SIBGRAPI), 2016 29th SIBGRAPI Conference on. 〈10.1109/SIBGRAPI.2016.044〉. 〈hal-01616394〉

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