10.1.1.14.1476

10.1.1.14.1476 - International Journal of Computer Vision...

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Unformatted text preview: International Journal of Computer Vision 43(1), 727, 2001 c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Contour and Texture Analysis for Image Segmentation JITENDRA MALIK, SERGE BELONGIE, THOMAS LEUNG AND JIANBO SHI Computer Science Division, University of California at Berkeley, Berkeley, CA 94720-1776, USA Received December 28, 1999; Revised February 23, 2001; Accepted February 23, 2001 Abstract. This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons . Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown. Keywords: segmentation, texture, grouping, cue integration, texton, normalized cut 1. Introduction To humans, an image is not just a random collection of pixels; it is a meaningful arrangement of regions and objects. Figure 1 shows a variety of images. De- spite the large variations of these images, humans have no problem interpreting them. We can agree about the different regions in the images and recognize the differ- ent objects. Human visual grouping was studied exten- sively by the Gestalt psychologists in the early part of the 20th century (Wertheimer, 1938). They identified several factors that lead to human perceptual group- ing: similarity, proximity, continuity, symmetry, par- allelism, closure and familiarity. In computer vision, these factors have been used as guidelines for many grouping algorithms. The most studied version of grouping in computer vi- sion is image segmentation. Image segmentation tech- niques can be classified into two broad families (1) region-based, and (2) contour-based approaches. Region-based approaches try to find partitions of the image pixels into sets corresponding to coherent im- Present address: Compaq Cambridge Research Laboratory. Present address: Robotics Institute, Carnegie Mellon University. age properties such as brightness, color and texture. Contour-based approaches usually start with a first stage of edge detection, followed by a linking process that seeks to exploit curvilinear continuity....
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10.1.1.14.1476 - International Journal of Computer Vision...

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