Image segmentation : principles, techniques, and applications / Tao Lei, Asoke K. Nandi.

By: Lei, Tao (Professor) [author.]
Contributor(s): Nandi, Asoke Kumar [author.]
Language: English Publisher: Hoboken, NJ : Wiley, 2023Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119859000; 9781119859031; 1119859034; 9781119859024; 1119859026; 9781119859048; 1119859042Subject(s): Image segmentationGenre/Form: Electronic books.DDC classification: 006.6 LOC classification: TA1638.4Online resources: Full text is available at Wiley Online Library Click here to view.
Contents:
Table of Contents Preface About the Authors List of Abbreviations Part One: Principle 1 Introduction to Image Segmentation 2 Principles of Clustering 3 Principles of Mathematical Morphology 4 Principles of Neural Network Part Two: Methods 5 Fast and Robust Image Segmentation Using Clustering 6 Fast Image Segmentation Using Watershed Transform 7 Superpixel-based Fast Image Segmentation Part Three: Application 8 Image Segmentation for Traffic Scene Analysis 9 Image Segmentation for Medical Analysis 10 Image Segmentation for Remote Sensing Analysis 11 Image Segmentation for Material Analysis
Summary: "Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors--such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression--to assist graduate students and researchers apply and improve image segmentation in their work."-- Provided by publisher.
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Includes index.

Table of Contents

Preface

About the Authors

List of Abbreviations

Part One: Principle

1 Introduction to Image Segmentation

2 Principles of Clustering

3 Principles of Mathematical Morphology

4 Principles of Neural Network

Part Two: Methods

5 Fast and Robust Image Segmentation Using Clustering

6 Fast Image Segmentation Using Watershed Transform

7 Superpixel-based Fast Image Segmentation

Part Three: Application

8 Image Segmentation for Traffic Scene Analysis

9 Image Segmentation for Medical Analysis

10 Image Segmentation for Remote Sensing Analysis

11 Image Segmentation for Material Analysis

"Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors--such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression--to assist graduate students and researchers apply and improve image segmentation in their work."-- Provided by publisher.

About the Author
Tao Lei, Professor, School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, China. His research interests include image processing, pattern recognition, and machine learning and he has authored and co-authored more than 100 research papers.

Asoke K. Nandi, Professor, Department of Electronic and Electrical Engineering, Brunel University London, UK. He is also Distinguished Visiting Professor, Xi’an Jiaotong University, China. Professor Nandi has authored over 600 technical publications, including 280 journal papers as well as five books.

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