A Guide to Convolutional Neural Networks for Computer Vision

Computer vision has become increasingly important and effective in recent years due to its
wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and
medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks,
such as image classification, localization, and detection, are the core building blocks of many of
these applications, and recent developments in Convolutional Neural Networks (CNNs) have
led to outstanding performance in these state-of-the-art visual recognition tasks and systems.
As a result, CNNs now form the crux of deep learning algorithms in computer vision.
This self-contained guide will benefit those who seek to both understand the theory behind
CNNs and to gain hands-on experience on the application of CNNs in computer vision .

It provides a comprehensive introduction to CNNs starting with the essential concepts behind
neural networks: training, regularization, and optimization of CNNs. The book also discusses a
wide range of loss functions, network layers, and popular CNN architectures, reviews the different
techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries
that are commonly used in computer vision. Further, this text describes and discusses case studies
that are related to the application of CNN in computer vision, including image classification,
object detection, semantic segmentation, scene understanding, and image generation.
This book is ideal for undergraduate and graduate students, as no prior background knowledge
in the field is required to follow the material, as well as new researchers, developers, engineers,
and practitioners who are interested in gaining a quick understanding of CNN models [1].

[1]        S. Khan, H. Rahmani, S. A. A. Shah, and M. Bennamoun, A Guide to Convolutional Neural Networks for Computer Vision, vol. 8, no. 1. 2018. doi: 10.2200/s00822ed1v01y201712cov015.