Image Segmentation Combining Edge and Texture Information

Image Segmentation Combining Edge and Texture Information

Abstract

The watershed transform is a conventional tool for the segmentation of images. Watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In this paper we describe a new image segmentation algorithm that integrates the measure of spatial variations in texture with the intensity gradients and consists of a number of conceptual stages. In the first stage, texture representation is calculated using vector summation of complex cell responses in different preferred orientations. In the second stage, gradient images are computed for each of the texture features, as well as for grey scale intensity. These gradients are efficiently estimated using a new proposed algorithm based on a hypothesis model of the human visual system. After that, combining these gradient images, a region gradient which highlights the region boundaries is obtained. Watershed transform of the region gradients properly segment the identified regions. Adaptive thresholding on rotational texture features is used to the problem of over segmentation. The combined algorithm produces effective texture and intensity based segmentation for natural and textured images.

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