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Edge detection is an important image processing operation with applications such as 3D reconstruction, recognition, image enhancement, image restoration and compression. Several edge detectors have been developed in the past decades although no single edge detector is best suited for all applications [S]. This paper presents a new concept in edge detection that is better suited for application-specific image processing. The grayscale or multi-bit image is mapped to a set of several binary images. This is followed by the application of edge detection algorithms on these binary images and a fusion of the individual edge maps. A novel polynomial based binarization method is also presented. An evolutionary approach to the fusion of edgemaps renders the algorithm adaptive. Experimental results have shown that this new concept has several advantages. It produces edges of better quality; it can be used in a dasiaprogressivepsila manner to save on computations and increase usability. Further, it can be used to take advantage of multiple popular edge detection algorithms (Danahy et al., 2007; Danahy et al., 2006; Danahy, 2006).
Machine Learning and Cybernetics, 2008 International Conference on (Volume:7 )
Date of Conference: 12-15 July 2008