Deep Learning in Visual Computing: Explanations and Examples

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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.

Author(s): Hassan Ugail
Publisher: CRC Press/Science Publishers
Year: 2022

Language: English
Pages: 139
City: Boca Raton

Cover
Title Page
Copyright Page
Dedication
Preface
Acknowledgments
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
Index
About the Author