Edge detection by using edge density and eleven algorithm comparisons in three types of color images

Abstract

Edge detection in grey scale image processing is a traditional research subject, but recently more and more researchers make efforts on the edge detection in color images. This paper presents a novel edge detection algorithm using the local, nonparametric estimation of the color image density. The method firstly analyses the edge shape information provided by the local probability distribution of the color image both in the horizontal and vertical directions respectively, then it obtains the modulus for the edge detection in the color image. With the increasing of window size, the other types of distributions can be simplified to the three types of the distributions presented in this study. In experiements, eleven different edge detection algorithms are compared for the three types of color images: smooth surface objects with a few edges; thin (or lines and curves) objects with many edges; and rough surface objects with more edges. And the algorithms include fractional, the first and the second order differential operators and other non-differential ones. Experiments show that the studied method is efficient.for edge extracting in a color image, and can give a satisfactory edge detection result in most cases.