Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments
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Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments

Alex Noel Joseph Raj (Shantou University, China), Vijayalakshmi G. V. Mahesh (BMS Institute of Technology and Management, India) and Ruban Nersisson (Vellore Institute of Technology, India)
Projected Release Date: December, 2020|Copyright: © 2021 |Pages: 400|DOI: 10.4018/978-1-7998-6690-9
ISBN13: 9781799866909|ISBN10: 1799866904|EISBN13: 9781799866923|ISBN13 Softcover: 9781799866916

Description

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task.

The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Convolutional Neural Networks
  • Deep Learning
  • Deep Neural Network
  • Disease Detection
  • Edge Enhancement
  • GAN Architecture
  • Image Processing
  • Image Segmentation
  • Machine Learning
  • Mobile Applications
  • U-Net Architecture
  • Ultrasound

Table of Contents and List of Contributors

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