A beginner's guide to image shape feature extraction techniques / / Jyotismita Chaki, Nilanjan Dey

• Autore: Chaki Jyotismita
• Pubblicazione: Boca Raton, FL, : CRC PRESS, 2019
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (147 pages)
• Disciplina: 006.37
• Soggetto topico: Computer vision; Pattern recognition systems
• Altri autori: DeyNilanjan <1984->
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Authors -- 1: Introduction to Shape Feature -- 1.1 Introduction -- 1.1.1 4-Neighborhood -- 1.1.2 d-Neighborhood -- 1.1.3 8-Neighborhood -- 1.1.4 Connectivity -- 1.1.5 Connected Components -- 1.2 Importance of Shape Features -- 1.3 Properties of Efficient Shape Features -- 1.4 Types of Shape Features -- 1.4.1 Contour-Based Shape Representation and Description Techniques -- 1.4.1.1 Global Methods -- 1.4.1.2 Structural Methods -- 1.4.1.3 Limitations of the Structural Approach -- 1.4.2 Region-Based Shape Representation and Description Techniques -- 1.5 Summary -- References -- 2: One-Dimensional Function Shape Features -- 2.1 Complex Coordinate (ComC) -- 2.2 Centroid Distance Function (CDF) -- 2.3 Tangent Angle (TA) -- 2.4 Contour Curvature (CC) -- 2.5 Area Function (AF) -- 2.6 Triangle Area Representation (TAR) -- 2.7 Chord Length Function (CLF) -- 2.8 Summary -- References -- 3: Geometric Shape Features -- 3.1 Center of Gravity (CoG) -- 3.2 Axis of Minimum Inertia (AMI) -- 3.3 Average Bending Energy (ABE) -- 3.4 Eccentricity -- 3.4.1 Principal Axes Method -- 3.4.2 Minimum Bounding Rectangle (MBR) -- 3.5 Circularity Ratio (CR) -- 3.6 Ellipticity -- 3.6.1 Ellipse Variance (EV) -- 3.6.2 Ellipticity Based on Moment Invariants -- 3.7 Rectangularity -- 3.7.1 Smallest Bounding Rectangle (SBR) -- 3.7.2 Rectangular Discrepancy Method (RDM) -- 3.7.3 Robust Smallest Bounding Rectangle (RSBR) -- 3.8 Convexity -- 3.9 Solidity -- 3.10 Euler Number (EN) -- 3.11 Profiles -- 3.12 Hole Area Ratio (HAR) -- 3.13 Summary -- References -- 4: Polygonal Approximation Shape Features -- 4.1 Merging Method (MM) -- 4.1.1 Distance Threshold Method (DTM) -- 4.1.2 Tunnelling Method (TM) -- 4.1.3 Polygon Evolution by Vertex Deletion (PEVD) -- 4.2 Splitting Method (SM). ; 4.3 Minimum Perimeter Polygon (MPP) -- 4.3.1 Data Preparation for MPP -- 4.3.2 MPP Algorithm -- 4.4 Dominant Point (DP) Detection -- 4.5 K-means Method -- 4.6 Genetic Algorithm (GA) -- 4.6.1 Encoding -- 4.6.2 Fitness -- 4.6.3 Genetic Operators or Control Parameters -- 4.7 Ant Colony Optimization (ACO) Method -- 4.7.1 Initialization -- 4.7.2 Node Transition Rule -- 4.7.3 Pheromone Updating Rule -- 4.7.4 Stopping Criterion -- 4.8 Tabu Search (TS) -- 4.8.1 Initialization -- 4.8.2 Definition of Moves -- 4.8.3 Aspiration Criteria (AC) -- 4.9 Summary -- References -- 5: Spatial Interrelation Shape Features -- 5.1 Adaptive Grid Resolution (AGR) -- 5.2 Bounding Box (BB) -- 5.3 Convex Hull (CH) -- 5.4 Chain Code (CC) -- 5.4.1 Basic -- 5.4.2 Differential -- 5.4.3 Re-sampling -- 5.4.4 Vertex -- 5.4.5 Chain Code Histogram (CCH) -- 5.5 Smooth Curve Decomposition (SCD) -- 5.6 Beam Angle Statistics (BAS) -- 5.7 Shape Matrix (SM) -- 5.7.1 Square Model -- 5.7.2 Polar Model -- 5.8 Shape Context (SC) -- 5.9 Chord Distribution (CD) -- 5.10 Shock Graphs (SG) -- 5.11 Summary -- References -- 6: Moment Shape Feature -- 6.1 Contour Moment (CM) -- 6.2 Geometric Invariant Moment (GIM) -- 6.3 Zernike Moment (ZM) -- 6.4 Radial Chebyshev Moment (RCM) -- 6.5 Legendre Moment (LM) -- 6.6 Homocentric Polar-Radius Moment (HPRM) -- 6.7 Orthogonal Fourier-Mellin Moment (OFMM) -- 6.8 Pseudo-Zernike Moment (PZM) -- 6.9 Summary -- References -- 7: Scale-Space Shape Features -- 7.1 Curvature Scale Space (CSS) -- 7.1.1 Extreme Curvature Scale Space (ECSS) -- 7.1.2 Direct Curvature Scale Space (DCSS) -- 7.1.3 Affine Resilient Curvature Scale Space (ARCSS) -- 7.2 Morphological Scale Space (MSS) -- 7.3 Intersection Points Map (IPM) -- 7.4 Summary -- References -- 8: Shape Transform Domain Shape Feature -- 8.1 Fourier Descriptors -- 8.1.1 One-Dimensional Fourier Descriptors. ; 8.1.2 Region-Based Fourier Descriptor -- 8.2 Wavelet Transform -- 8.3 Angular Radial Transformation (ART) -- 8.4 Shape Signature Harmonic Embedding -- 8.5 R-Transform -- 8.6 Shapelet Descriptor (SD) -- 8.7 Summary -- References -- 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 -- References -- Index.
• Sommario/riassunto: 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.
• Titolo autorizzato: A beginner's guide to image shape feature extraction techniques
• ISBN: 1-000-03430-5; 0-429-28779-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910828945303321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent signal processing and data analysis.