Abstract: The edge detection on the images is so important for image processing. It is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictness, but require excessive post-processing. Proposed CNN technique is used to realize edge detection task it takes the advantage of momentum features extraction, it can process any input image of any size with no more training required, the results are very promising when compared to both classical methods and other ANN based methods

Mohamed A. El-Sayed, Yarub A. Estaitia and Mohamed A. Khafagy, “Automated Edge Detection Using Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 4(10), 2013. http://dx.doi.org/10.14569/IJACSA.2013.041003

@article{El-Sayed2013,
title = {Automated Edge Detection Using Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.041003},
url = {http://dx.doi.org/10.14569/IJACSA.2013.041003},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {10},
author = {Mohamed A. El-Sayed and Yarub A. Estaitia and Mohamed A. Khafagy}
}