A Beginner's Guide to Image Shape Feature Extraction Techniques

ISBN-13: 9781032090634 / Angielski / Miękka / 2021 / 148 str.

Nilanjan Dey

Opis

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.

Spis treści

Chapter 1: Introduction to Shape Feature1.1 Introduction to Shape Feature1.2 Importance of Shape Features1.3 Properties of Efficient Shape Features1.4 Types of Shape features1.5 SummaryChapter 2: One Dimensional Function Shape Features2.1 Complex Coordinate2.2 Centroid Distance Function2.3 Tangent Angle2.4 Contour Curvature2.5 Area Function2.6 Triangle Area Representation2.7 Cord Length Function2.8 SummaryChapter 3: Geometric Shape Features3.1 Center of Gravity3.2 Axis of Minimum Inertia3.3 Average Bending Energy3.4 Eccentricity3.5 Circularity ratio3.6 Ellipticity3.7 Rectangularity3.8 Convexity3.9 Solidity3.10 Euler Number3.11 Profiles3.12 Hole Area Ratio3.13 SummaryChapter 4: Polygonal Approximation Shape Features4.1 Merging Method4.2 Splitting Method4.3 Minimum Perimeter Polygon4.4 Dominant Point Detection4.5 K-means Method4.6 Genetic Algorithm4.7 Ant Colony Optimization Method4.8 Tabu Search Method4.9 SummaryChapter 5: Spatial Interrelation Shape Features5.1 Adaptive Grid Resolution5.2 Bounding Box5.3 Convex-Hull5.4 Chain Code5.5 Smooth Curve Decomposition5.6 Beam Angle Statistics5.7 Shape Matrix5.8 Shape Context5.9 Chord Distribution5.10 Shock Graphs5.11 Summary Chapter 6: Moments Shape Feature6.1 Contour Moment6.2 Geometric Invariant Moment6.3 Zernike Moment6.4 Radial Chebyshev Moment6.5 Legendre Moment6.6 Homocentric Polar-Radius Moment6.7 Orthogonal Fourier-Mellin Moment6.8 Pseudo-Zernike Moment6.9 SummaryChapter 7: Scale Space Shape Features7.1 Curvature Scale Space7.2 Morphological Scale Space7.3 Intersection Points Map7.4 SummaryChapter 8: Shape Transform Domain Shape Features8.1 Fourier Descriptors8.2 Wavelet Transforms8.3 Angular Radial Transformation8.4 Shape Signature Harmonic Embedding8.5 ¿-Transform8.6 Shapelets Descriptor8.7 SummaryChapter 9: Applications of Shape Features9.1 Digit Recognition9.2 Character Recognition 9.3 Fruit Recognition9.4 Leaf Recognition9.5 Hand Gesture Recognition9.6 Summary

O autorze

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).