Deep Learning for Image Processing Applications
eBook - PDF

Deep Learning for Image Processing Applications

  1. 284 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Deep Learning for Image Processing Applications

About this book

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, 'Deep Learning for Image Processing Applications', is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

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Yes, you can access Deep Learning for Image Processing Applications by D.J. Hemanth,V. Vieira Estrela,Vania Vieira Estrela in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title Page
  2. Preface
  3. Contents
  4. About the Editors
  5. Mind, Machine, and Image Processing
  6. Deep Neural Networks for Image Classification
  7. Virtual Robotic Arm Control with Hand Gesture Recognition and Deep Learning Strategies
  8. Intelligent Image Retrieval via Deep Learning Techniques
  9. Advanced Stevia Disease Detection Using Deep Learning
  10. Analysis of Tuberculosis Images Using Differential Evolutionary Extreme Learning Machines (DE-ELM)
  11. Object Retrieval with Deep Convolutional Features
  12. Hierarchical Object Detection with Deep Reinforcement Learning
  13. Big Data & Deep Data: Minding the Challenges
  14. Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SF-CNNLS) for Intra-Class Variation of Vehicle Type Recognition
  15. On the Prospects of Using Deep Learning for Surveillance and Security Applications
  16. Super-Resolution of Long Range Captured Iris Image Using Deep Convolutional Network
  17. Subject Index
  18. Author Index