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This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology. Chapter 1: Introduction to Shape Feature 1.1 Introduction to Shape Feature 1.2 Importance of Shape Features 1.3 Properties of Efficient Shape Features 1.4 Types of Shape features 1.5 Summary Chapter 2: One Dimensional Function Shape Features 2.1 Complex Coordinate 2.2 Centroid Distance Function 2.3 Tangent Angle 2.4 Contour Curvature 2.5 Area Function 2.6 Triangle Area Representation 2.7 Cord Length Function 2.8 Summary Chapter 3: Geometric Shape Features 3.1 Center of Gravity 3.2 Axis of Minimum Inertia 3.3 Average Bending Energy 3.4 Eccentricity 3.5 Circularity ratio 3.6 Ellipticity 3.7 Rectangularity 3.8 Convexity 3.9 Solidity 3.10 Euler Number 3.11 Profiles 3.12 Hole Area Ratio 3.13 Summary Chapter 4: Polygonal Approximation Shape Features 4.1 Merging Method 4.2 Splitting Method 4.3 Minimum Perimeter Polygon 4.4 Dominant Point Detection 4.5 K-means Method 4.6 Genetic Algorithm 4.7 Ant Colony Optimization Method 4.8 Tabu Search Method 4.9 Summary Chapter 5: Spatial Interrelation Shape Features 5.1 Adaptive Grid Resolution 5.2 Bounding Box 5.3 Convex-Hull 5.4 Chain Code 5.5 Smooth Curve Decomposition 5.6 Beam Angle Statistics 5.7 Shape Matrix 5.8 Shape Context 5.9 Chord Distribution 5.10 Shock Graphs 5.11 Summary 　 Chapter 6: Moments Shape Feature 6.1 Contour Moment 6.2 Geometric Invariant Moment 6.3 Zernike Moment 6.4 Radial Chebyshev Moment 6.5 Legendre Moment 6.6 Homocentric Polar-Radius Moment 6.7 Orthogonal Fourier-Mellin Moment 6.8 Pseudo-Zernike Moment 6.9 Summary Chapter 7: Scale Space Shape Features 7.1 Curvature Scale Space 7.2 Morphological Scale Space 7.3 Intersection Points Map 7.4 Summary Chapter 8: Shape Transform Domain Shape Features 8.1 Fourier Descriptors 8.2 Wavelet Transforms 8.3 Angular Radial Transformation 8.4 Shape Signature Harmonic Embedding 8.5 -Transform 8.6 Shapelets Descriptor 8.7 Summary Chapter 9: Applications of Shape Features 9.1 Digit Recognition 9.2 Character Recognition 9.3 Fruit Recognition 9.4 Leaf Recognition 9.5 Hand Gesture Recognition 9.6 Summary Dr. Jyotismita Chaki is an Asst. Professor in the Department of Information Technology and Engineering in Vellore Institute of Technology, Vellore, India. She has done her PhD (Engg) in digital image processing from Jadavpur University, Kolkata, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Data mining, Machine learning. She has published one book and 22 international conferences and journal papers. She has also served as a Program Committee member of 2nd International Conference on Advanced Computing and Intelligent Engineering 2017 (ICACIE-2017), 4TH International Conference on Image Information Processing (ICIIP-2017). Dr. Nilanjan Day was born in Kolkata, India, in 1984. He received his B.Tech. degree in Information Technology from West Bengal University of Technology in 2005,M.Tech. in InformationTechnology in 2011 fromthe same University and Ph.D. in digital image processing in 2015 from Jadavpur University, India. In 2011, he was appointed as an Asst. Professor in the Department of Information Technology at JIS College of Engineering, Kalyani, India followed by Bengal College of Engineering College, Durgapur, India in 2014. He is now employed as an Asst. Professor in Department of Information Technology, Techno India College of Technology, India. His research topic is signal processing, machine learning and information security. Dr. Dey is an Associate Editor of IEEE ACCESS and is currently the Editor in-Chief of the International Journal of Ambient Computing and Intelligence, and Series Editor of Springer Tracts in Nature-Inspired Computing (STNIC).

A Beginner's Guide to Image Multi-Level Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. Explains basic concepts and the implementation of Image Multi-Level Thresholding (grayscale and RGB images)Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcomeDescribes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholdingIllustrates integration of the thresholding technique with bio-inspired algorithmsIncludes current findings and future directions of image multi-level thresholding and its practical implementationEmphasizes the need for multi-level thresholding with suitable examples The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.

A Beginner's Guide to Image MultiLevel Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. Explains basic concepts and the implementation of Image MultiLevel Thresholding (grayscale and RGB images) Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcome Describes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholding Illustrates integration of the thresholding technique with bio-inspired algorithms Includes current findings and future directions of image multilevel thresholding and its practical implementation Emphasizes the need for multilevel thresholding with suitable examples The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.