Deep Learning in Computer Vision-Principles and Applications

✍ Scribed by Mahmoud Hassaballah (Editor); Ali Ismail Awad (Editor)

• Publisher: CRC Press
• Year: 2020
• Leaves: 339
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Chapter 1 Accelerating the CNN Inference on FPGAs

[Kamel Abdelouahab, Maxime Pelcat, and François Berry]

Chapter 2 Object Detection with Convolutional Neural Networks

[Kaidong Li, Wenchi Ma, Usman Sajid, Yuanwei Wu, and

Guanghui Wang]

Chapter 3Efficient Convolutional Neural Networks for Fire Detection in

Surveillance Applications

[Khan Muhammad, Salman Khan, and Sung Wook Baik]

Chapter 4A Multi-biometric Face Recognition System Based on

Multimodal Deep Learning Representations

[Alaa S. Al-Waisy, Shumoos Al-Fahdawi, and Rami Qahwaji]

Chapter 5Deep LSTM-Based Sequence Learning Approaches for Action

and Activity Recognition

[Amin Ullah, Khan Muhammad, Tanveer Hussain,

Miyoung Lee, and Sung Wook Baik]

Chapter 6Deep Semantic Segmentation in Autonomous Driving

[Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab,

Mahmoud Hassaballah, and Mohamed ElHelw]

Chapter 7Aerial Imagery Registration Using Deep Learning for

UAV Geolocalization

[Ahmed Nassar, and Mohamed ElHelw]

Chapter 8Applications of Deep Learning in Robot Vision

[Javier Ruiz-del-Solar and Patricio Loncomilla]

Chapter 9Deep Convolutional Neural Networks: Foundations and

Applications in Medical Imaging

[Mahmoud Khaled Abd-Ellah, Ali Ismail Awad,

Ashraf A. M. Khalaf, and Hesham F. A. Hamed]

Chapter 10Lossless Full-Resolution Deep Learning Convolutional

Networks for Skin Lesion Boundary Segmentation

[Mohammed A. Al-masni, Mugahed A. Al-antari, and Tae-Seong Kim]

Chapter 11Skin Melanoma Classification Using Deep Convolutional

Neural Networks

[Khalid M. Hosny, Mohamed A. Kassem, and Mohamed M. Foaud]