%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.22.21.00
%2 sid.inpe.br/sibgrapi/2016/07.22.21.00.42
%@doi 10.1109/SIBGRAPI.2016.044
%T Stochastic hierarchical watershed cut based on disturbed topographical surface
%D 2016
%A Pimentel Filho, Carlos Alberto F.,
%A Araujo, Arnaldo de Albuquerque,
%A Cousty, Jean,
%A Guimaraes, Silvio Jamil F.,
%A Najman, Laurent,
%@affiliation Audio-Visual Information Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas
%@affiliation NPDI/DCC/UFMG - Federal University of Minas Gerais - Computer Science Department - Belo Horizonte, MG, Brazil
%@affiliation Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France
%@affiliation Audio-Visual Information Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas
%@affiliation Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K watershed, stochastic segmentation, hierarchical segmentation, mathematical morphology.
%X 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.
%@language en
%3 PID4373571.pdf