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Textured Image Segmentation Based on Spatial Dependence using a Markov Random Field Model

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2 Author(s)
William Robson Schwartz ; University of Maryland, Computer Science Department, College Park, MD, USA, 20742-3275 ; Helio Pedrini

Image segmentation is a primary step in many computer vision tasks. Although many segmentation methods have been proposed in the last decades, there is no generic method that can be applied in a great variety of images. This work presents a new image segmentation method using texture features extracted by wavelet transforms combined with spatial dependence modeled by a Markov random field (MRF). The method initially produces a coarse segmentation, which is refined through a relaxation method based on a new energy function. A set of textured images is used to demonstrate the effectiveness of the proposed method.

Published in:

2006 International Conference on Image Processing

Date of Conference:

8-11 Oct. 2006