Deep Learning in Visual Computing: Explanations and Examples

Ugail, Hassan

ISBN 10: 0367549638 / ISBN 13: 9780367549633

Published by CRC Press 2022-07, 2022

About this Item

Seller Inventory # 6666-IUK-9780367549633

Bibliographic Details

• Title: Deep Learning in Visual Computing: Explanations and Examples
• Publisher: CRC Press 2022-07
• Publication Date: 2022
• Language: English
• ISBN 10: 0367549638
• ISBN 13: 9780367549633
• Binding: PF
• Condition: New

About this title

Synopsis

Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing.

This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.

About the Author

Prof Hassan Ugail is Director of the Centre for Visual Computing at the University of Bradford, UK. He is a renowned computer scientist in the area of visual computing and artificial intelligence (AI). He is an advocate of AI for helping to tackle real world issues in the areas of digital health, innovative engineering and sustainable societies in general. More specifically, he works in the area of human biometrics especially the development of cutting-edge AI solutions for biometric face recognition. His most recent work in this area includes helping to unravel the real identity of the two Russian spies at the heart of the Salisbury Novichok poisoning case - one of the biggest international stories of 2018.