Deep Learning in Visual Computing: Explanations and Examples

by Hassan Ugail

Overview

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.

Product Details

ISBN-13:	9780367549626
Publisher:	CRC Press
Publication date:	07/07/2022
Pages:	140
Product dimensions:	5.44(w) x 8.50(h) x (d)

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.

Table of Contents

1. Introduction 2. The Foundations of Deep Learning 3. Deep Learning Models for Visual Computing 4. Deep Face Recognition 5. Age Estimation from Face Images using Deep Learning 6. The Nose and Ethnicity 7. Analysis of Skin Burns using Deep Learning 8. Deep Learning Approaches to Cancer Diagnosis using Histopathological Images 9. A Deep Transfer Learning Model for the Analysis of Electrocardiograms 10. Advances in Visual Computing through Deep Learning 11. Frontiers and Challenges in Deep Learning for Visual Computing