A Novel Method for Edge Detection Using 2 Dimensional Gamma Distribution
DOI : 10.3844/jcssp.2010.199.204
Journal of Computer Science
Volume 6, Issue 2
Problem statement: Edge detection is an important field in image processing. Edges characterize object boundaries and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. Approach: This study presented a novel method for edge detection using 2D Gamma distribution. Edge detection is traditionally implemented by convolving the image with masks. These masks are constructed using a first derivative, called gradient or second derivative called Laplacien. Thus, the problem of edge detection is therefore related to the problem of mask construction. We propose a novel method to construct different gradient masks from 2D Gamma distribution. Results: The different constructed masks from 2D Gamma distribution are applied on images and we obtained very good results in comparing with the well-known Sobel gradient and Canny gradient results. Conclusion: The experiment showed that the proposed method obtained very good results but with a big time complexity due to the big number of constructed masks.
© 2010 Ali El-Zaart. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.