Texture-Gradient-Based Contour Detection
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Texture-Gradient-Based Contour Detection Nasser Chaji1, 2 and Hassan Ghassemian1 1 Department 2 Department
of Electrical Engineering, Tarbiat Modares University, P.O. Box 14115-143, Tehran, Iran of Electrical and Communication Engineering, Birjand University, P.O. Box 97175-376, Birjand, Iran
Received 16 July 2005; Revised 4 February 2006; Accepted 1 April 2006 Recommended for Publication by Jiri Jan In this paper, a new biologically motivated method is proposed to effectively detect perceptually homogenous region boundaries. This method integrates the measure of spatial variations in texture with the intensity gradients. In the first stage, texture representation is calculated using the nondecimated complex wavelet transform. 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. Nonmaximum suppression and then thresholding with hysteresis is used to detect contour map from the region gradients. Natural and textured images with associated ground truth contour maps are used to evaluate the operation of the proposed method. Experimental results demonstrate that the proposed contour detection method presents more effective performance than conventional approaches. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
The ideal step function subject to white Gaussian noise is a frequently used edge model in many conventional edge detectors such as those mentioned by Canny [1], Shen and Castan [2], and Rakesh [3]. Using this model, any significant change in intensity values may be detected as an edge. Therefore conventional approaches may detect many spurious edges in textured regions where there is no boundary. As a result, they are not suitable for contour detection. There is evidence that human visual system is able to distinguish between contour of objects and edges originating from textured regions in its early stages of visual information processing [4–6]. The goal of our work is to develop a computational model of HVS that identifies perceptually homogenous region boundaries. It is not possible to build a computational HVS model for image processing applications directly from physiology of the HVS due to its tremendous complexity. Computational models introduced for different aspects of HVS were developed aiming observations from psychovisual experiments or sequential processing of the visual information in different layers of the HVS [7–9]. Models introduced for the nonclassical receptive field inhibition are examples developed in such a way [8]. Studies have shown that once a cell is activated by an optimal stimulus in its classical receptive field, simultaneously presented stimulus outside that field can have an effect
on the cell response. This mostly inhibitive effect is refe
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