"Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scopeof topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition"--
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
| Foreword |
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vii | |
| Preface |
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ix | |
| Editors Bio |
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xiii | |
| Contributors |
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xv | |
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Chapter 1 Accelerating the CNN Inference on FPGAs |
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1 | (40) |
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Chapter 2 Object Detection with Convolutional Neural Networks |
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41 | (22) |
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Chapter 3 Efficient Convolutional Neural Networks for Fire Detection in Surveillance Applications |
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63 | (26) |
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Chapter 4 A Multi-biometric Face Recognition System Based on Multimodal Deep Learning Representations |
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89 | (38) |
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Chapter 5 Deep LSTM43ased Sequence Learning Approaches for Action and Activity Recognition |
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127 | (24) |
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Chapter 6 Deep Semantic Segmentation in Autonomous Driving |
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151 | (32) |
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Chapter 7 Aerial Imagery Registration Using Deep Learning for UAV Geolocalization |
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183 | (28) |
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Chapter 8 Applications of Deep Learning in Robot Vision |
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211 | (22) |
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Chapter 9 Deep Convolutional Neural Networks: Foundations and Applications in Medical Imaging |
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233 | (28) |
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Chapter 10 Lossless Full-Resolution Deep Learning Convolutional Networks for Skin Lesion Boundary Segmentation |
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261 | (30) |
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Chapter 11 Skin Melanoma Classification Using Deep Convolutional Neural Networks |
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291 | (24) |
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| Index |
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315 | |
Mahmoud Hassaballah received the Doctor of Engineering (D. Eng.) in Computer Science from Ehime University, Japan in 2011. He was a visiting scholar with the Department of Computer & Communication Science, Wakayama University, Japan and GREAH laboratory, Le Havre Normandie University, France. He is currently an Associate Professor of Computer Science at the Faculty of Computers and Information, South Valley University, Egypt. His research interests include feature extraction, object detection/recognition, artificial intelligence, biometrics, image processing, computer vision, machine learning, and data hiding.
Ali Ismail Awad is currently an Associate Professor (Docent) with the Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden, where he also serves as a Coordinator of the Master Programme in Information Security. He is a Visiting Researcher with the University of Plymouth, United Kingdom. He is also an Associate Professor with the Electrical Engineering Department, Faculty of Engineering, Al-Azhar University at Qena, Qena, Egypt. His research interests include information security, Internet-of-Things security, image analysis with applications in biometrics and medical imaging, and network security.
