Edge detection of color images using directional operators

This paper discusses an approach for detecting edges in color images. A color image is represented by a vector field, and the color image edges are detected as differences in the local vector statistics. These statistical differences can include local variations in color or spatial image properties. The proposed approach can easily accommodate concepts, such as multiscale edge detection, as well as the latest developments in vector order statistics for color image processing. A distinction between the proposed approach and previous approaches for color edge detection using vector order statistics is that, besides the edge magnitude, the local edge direction is also provided. Note that edge direction information is a relevant feature to a variety of image analysis tasks (e.g., texture analysis).

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[3]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[4]  Glenn Healey,et al.  Segmenting images using normalized color , 1992, IEEE Trans. Syst. Man Cybern..

[5]  Andrew P. Witkin,et al.  Scale-space filtering: A new approach to multi-scale description , 1984, ICASSP.

[6]  S. Grossberg Neural Networks and Natural Intelligence , 1988 .

[7]  Panos E. Trahanias,et al.  Color edge detection using vector order statistics , 1993, IEEE Trans. Image Process..