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		<doi>10.1109/SIBGRAPI.2016.044</doi>
		<citationkey>PimentelFoAraCouGuiNaj:2016:StHiWa</citationkey>
		<title>Stochastic hierarchical watershed cut based on disturbed topographical surface</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
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		<author>Pimentel Filho, Carlos Alberto F.,</author>
		<author>Araujo, Arnaldo de Albuquerque,</author>
		<author>Cousty, Jean,</author>
		<author>Guimaraes, Silvio Jamil F.,</author>
		<author>Najman, Laurent,</author>
		<affiliation>Audio-Visual Information Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas</affiliation>
		<affiliation>NPDI/DCC/UFMG -  Federal University of Minas Gerais - Computer Science Department -  Belo Horizonte, MG, Brazil</affiliation>
		<affiliation>Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France</affiliation>
		<affiliation>Audio-Visual Information Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas</affiliation>
		<affiliation>Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>silvio.jamil@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>IEEE Computer Society´s Conference Publishing Services</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>watershed, stochastic segmentation, hierarchical segmentation, mathematical morphology.</keywords>
		<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.</abstract>
		<language>en</language>
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		<usergroup>silvio.jamil@gmail.com</usergroup>
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