A new BiGaussian edge filter

Abstract

Edge detection has been the foremost step in image processing and computer vision, because an edge representation drastically reduces the amount of data to be processed. Although classical methods of edge detection like Sobel, Canny, etc. are simple to use but has a dilemma between noise removal and edge localization. If noise is to be removed by using a low pass filter then edges are blurred. However, if edges have to be preserved then noise severly corrupts the edge map. In this paper, we have proposed a new method of edge detection, BiGaussian edge Filter, which simultaneously removes noise from real life images, while generating well localized edges. We have compared our method using images form Berkely's segmentation data set. Experimental results show the robustness of our method to noise in real life images. © 2012 Springer Science+Business Media B.V.