Edge detection plays an important role in imageEdge detection plays an important role in image processing, pattern recognition and computer vision applications. Most of edge detection schemes are based on finding maximum in the first derivative of the image function or zero crossings in the second derivative of the image function. Various methods of edge detection for color images, including techniques extended from monochrome edge detection as well as vector space methods are presented. This research presents a comparative study on different methods of edge detection of color images. The methods are based on vector space, color space and numerical methods. Seven different colored images are test in this research. Performance is analyzed depending on Mean Square Error (MSE). The experimental results show that applying vector value (Jacobian method )will create a thick and disconnected edge with all operators Sobel, Prewitt and Log. While the least square method produce edges that are much thicker but continuous. The best performance was found when using YCbCr luminance (Y) and chrominance (Cb and Cr) method, the edges are sharpened, continuous, and not thickness. They are similar with Sobel and Prewitt operators nonetheless with some missing edges while it is better with Log operator.
Keywords: Edge detection; least square numerical method; Sobel operator; Prewitt operator; log operator,Jacobian eignvalue.
Sarah Behnam Aziz, Dalya Abdullah Anwer, Mardin A. Anwer
College of Sciences,
Computer Science &IT department