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

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

Alex Noel Joseph Raj, Vijayalakshmi G. V. Mahesh, Ruban Nersisson
Copyright: © 2021 |Pages: 381
ISBN13: 9781799866909|ISBN10: 1799866904|EISBN13: 9781799866923
DOI: 10.4018/978-1-7998-6690-9
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MLA

Raj, Alex Noel Joseph, et al., editors. Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments. IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6690-9

APA

Raj, A. J., Mahesh, V. G., & Nersisson, R. (Eds.). (2021). Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments. IGI Global. https://doi.org/10.4018/978-1-7998-6690-9

Chicago

Raj, Alex Noel Joseph, Vijayalakshmi G. V. Mahesh, and Ruban Nersisson, eds. Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6690-9

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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.

Table of Contents

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Front Materials
Title Page
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Copyright Page
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Advances in Computational Intelligence and Robotics (ACIR) Book Series
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Preface
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Acknowledgment
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Chapters
Back Materials
Compilation of References
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About the Contributors
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Index
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