%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.13.17.47
%2 sid.inpe.br/sibgrapi/2016/07.13.17.47.20
%@doi 10.1109/SIBGRAPI.2016.046
%T A Graph-Based Approach for Contextual Image Segmentation
%D 2016
%A Souza, Gustavo Botelho de,
%A Alves, Gabriel Marcelino,
%A Levada, Alexandre Luís Magalhães,
%A Cruvinel, Paulo Estevão,
%A Marana, Aparecido Nilceu,
%@affiliation Universidade Federal de São Carlos (UFSCar), Banco do Brasil
%@affiliation Universidade Federal de São Carlos (UFSCar), Embrapa Instrumentação
%@affiliation Universidade Federal de São Carlos (UFSCar)
%@affiliation Embrapa Instrumentação, Universidade Federal de São Carlos (UFSCar)
%@affiliation Universidade Estadual Paulista (UNESP), Universidade Federal de São Carlos (UFSCar)
%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 Min Cut-Max Flow, Graph Theory, Anisotropic Diffusion, Image Segmentation.
%X Image segmentation is one of the most important tasks in Image Analysis since it allows locating the relevant regions of the images and discarding irrelevant information. Any mistake during this phase may cause serious problems to the subsequent methods of the image-based systems. The segmentation process is usually very complex since most of the images present some kind of noise. In this work, two techniques are combined to deal with such problem: one derived from the graph theory and other from the anisotropic filtering methods, both emphasizing the use of contextual information in order to classify each pixel in the image with higher precision. Given a noisy grayscale image, an anisotropic diffusion filter is applied in order to smooth the interior regions of the image, eliminating noise without loosing much information of boundary areas. After that, a graph is built based on the pixels of the obtained diffused image, linking adjacent nodes (pixels) and considering the capacity of the edges as a function of the filter properties. Then, after applying the Ford-Fulkerson algorithm, the minimum cut of the graph is found (following the min cut-max flow theorem), segmenting the object of interest. The results show that the proposed approach outperforms the traditional and well-referenced Otsu's method.
%@language en
%3 PID4357791.pdf