@article {10.3844/jcssp.2010.199.204,
article_type = {journal},
title = {A Novel Method for Edge Detection Using 2 Dimensional Gamma Distribution},
author = {El-Zaart, Ali},
volume = {6},
number = {2},
year = {2010},
month = {Feb},
pages = {199-204},
doi = {10.3844/jcssp.2010.199.204},
url = {https://thescipub.com/abstract/jcssp.2010.199.204},
abstract = {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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}