Handbook of research on deep learning-based image analysis under constrained and unconstrained environments

著者

    • Raj, Alex Noel Joseph
    • Mahesh, Vijayalakshmi G. V.
    • Nersisson, Ruban

書誌事項

Handbook of research on deep learning-based image analysis under constrained and unconstrained environments

[edited by] Alex Noel Joseph Raj, Vijayalakshmi G.V. Mahesh, Ruban Nersisson

(Advances in computational intelligence and robotics (ACIR) book series)

Engineering Science Reference, an imprint of IGI Global, c2021

  • : [hardback]

タイトル別名

Deep learning-based image analysis under constrained and unconstrained environments

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注記

Includes bibliographical references (p. 344-371) and index

内容説明・目次

内容説明

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.

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