Standardization of edge magnitude in color images

Edge detection is a useful task in low-level image processing. The efficiency of many image processing and computer vision tasks depends on the perfection of detecting meaningful edges. To get a meaningful edge, thresholding is almost inevitable in any edge detection algorithm. Many algorithms reported in the literature adopt ad hoc schemes for this purpose. These algorithms require the threshold values to be supplied and tuned by the user. There are many high-level tasks in computer vision which are to be performed without human intervention. Thus, there is a need to develop a scheme where a single set of threshold values would give acceptable results for many color images. In this paper, an attempt has been made to devise such an algorithm. Statistical variability of partial derivatives at each pixel is used to obtain standardized edge magnitude and is thresholded using two threshold values. The advantage of standardization is evident from the results obtained

I. Introduction

Edge detection has been a challenging problem in low-level image processing. It becomes more challenging when color images are considered because of its multidimensional nature. Color images provide more information than grayscale images. Thus, more edge information is expected from a color edge detector than a grayscale edge detector [1]–[3]. In a grayscale image, edges are detected by detecting the discontinuities in the image surface, i.e., the discontinuities in the intensity of a sequence of pixels in a particular direction called gradient direction. The discontinuities in grayscale is easy to determine because gray values are partially ordered, but this freedom is not there in a color image. The simple difference between color vectors does not give the true distance between them. Sometimes, it is difficult to detect a low intensity [2] edge between two regions in grayscale, but in color image, the clarity is more because, without being much different in intensity there can be a substantial difference in hue. Almost 90% of edge information in a color image can be found in the corresponding grayscale image. However, the remaining 10% can still be vital in certain computer vision tasks [1]. Further, human perception of color picture is perceptually much richer than an achromatic picture [4].