ISBN: 9783659926693 - neural network and entropy algorithms for edge detection in sar images (6 risultati)

Paperback. Condizione: Brand New. 136 pages. 8.66x5.91x0.31 inches. In Stock.

Taschenbuch. Condizione: Neu. Neural Network And Entropy Algorithms For Edge Detection In SAR Images | Mohamed A. El-Sayed (u. a.) | Taschenbuch | 136 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659926693 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 136 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: El-Sayed Mohamed A.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.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.…