Neural Network And Entropy Algorithms For Edge Detection In SAR Images

Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to Synthetic Aperture Radar (SAR) imaging applications. It can be used in many applications such as segmentation, registration, feature extraction, and identification of objects in a scene. This book deals with the different types of applications of edge detection in real life. It presents a brief study of the fundamental concepts of the edge detection operation, and theories behind different edge detectors. Edge detection models based on first and second order derivatives are presented. Previous works on edge detection models are reviewed and simulated in Matlab. It shows an initial exploration of the effectiveness of using Convolutional Neural Networks (CNNs) for this task. CNNs exploit spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers. These techniques are object-independent so that the proposed method can be applied to other types of image processing such as classification and segmentation.

Autorentext

Dr. Mohamed A. El-Sayed has obtained his PhD degree in Computer Science in 2007. His research interests include image processing. He is the author of several books and articles published in reputed journals and is a member of different working groups. He is working in Fayoum Univ., Egypt. Currently, he is an As. Prof. at Taif Univ., Saudi Arabia.