Robust color edge detection through tensor voting

This paper presents a new method for color edge detection based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information via tensors in order to propagate them into a neighborhood through a voting process specifically designed for color edge detection by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorithm is more robust and has a similar performance in precision when compared with the state-of-the-art.