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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.28.00.23
%2 sid.inpe.br/sibgrapi/2018/10.28.00.23.25
%A Lima, Victor Cangelosi de,
%A Marengoni, Mauricio,
%@affiliation Universidade Presbiteriana Mackenzie
%@affiliation Universidade Presbiteriana Mackenzie
%T Segmentação em Imagens de Profundidade em Ambientes Controlados
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%D 2018
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%S Proceedings
%8 Oct. 29 - Nov. 1, 2018
%J Porto Alegre
%I Sociedade Brasileira de Computação
%C Foz do Iguaçu, PR, Brazil
%K segmentação, RGB-D, imagem de profundidade.
%X Assistive technologies combined with computer vision techniques have important relevance to aid the navigation of the visually impaired, allowing their social inclusion and safety. This paper proposes an efficient and precise system for segmentation of depth images, originated from the Kinect sensor. The algorithm can be used to identify obstacles for navigation purpose. The approach shows the use of graphs for segmentation avoiding costly post processing.
%@language pt
%3 indoor-depth-image.pdf


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