Detection of edges in color images: a review and evaluative comparison of state-of-the-art techniques

Authors:
Ajay Mittal;Sanjeev Sofat;Edwin Hancock
Affiliations:
Department of Comp. Science & Engg., PEC University of Technology, Chandigarh, India;Department of Comp. Science & Engg., PEC University of Technology, Chandigarh, India;Department of Computer Science, University of York, York, UK
Venue:
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Year:
2012

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Abstract

We present an evaluative review of various edge detection techniques for color images that have been proposed in the last two decades. The statistics shows that color images contain 10% additional edge information as compared to their gray scale counterparts. This additional information is crucial for certain computer vision tasks. Although, several reviews of the work on gray scale edge detection are available, color edge detection has few. The latest review on color edge detection is presented by Koschan and Abidi in 2005. Much advancement in color edge detection has been made since then, and thus, a thorough review of state-of-art color edge techniques is much needed. The paper makes a review and evaluation of various color edge detection techniques to quantify their accuracy and robustness against noise. It is found that Minimum Vector Dispersion (MVD) edge detector has the best edge detection accuracy and Robust Color Morphological Gradient-Median-Mean (RCMG-MM) edge detector has highest robustness against the noise.