LEADER 06405oam 2200529Mn 450 001 9910968353203321 005 20251116170857.0 010 $a1-000-03430-5 010 $a0-429-28779-8 024 8 $a10.1201/9780429287794 035 $a(CKB)4100000008780679 035 $a(MiAaPQ)EBC5839964 035 $a(OCoLC)1110719904$z(OCoLC)1111432528 035 $a(OCoLC-P)1110719904 035 $a(FlBoTFG)9780429287794 035 $a(EXLCZ)994100000008780679 100 $a20190803d2019 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 11$aA beginner's guide to image shape feature extraction techniques /$fJyotismita Chaki, Nilanjan Dey 205 $a1st ed. 210 $aBoca Raton, FL $cCRC PRESS$d2019 215 $a1 online resource (147 pages) 225 1 $aIntelligent signal processing and data analysis 311 08$a0-367-25439-5 320 $aIncludes bibliographical references and index. 327 $aCover -- 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). 327 $a4.3 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. 327 $a8.1.2 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. 330 $aThis book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology. 410 0$aIntelligent signal processing and data analysis. 606 $aComputer vision 606 $aPattern recognition systems 615 0$aComputer vision. 615 0$aPattern recognition systems. 676 $a006.37 700 $aChaki$b Jyotismita$01226856 701 $aDey$b Nilanjan$f1984-$0846728 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910968353203321 996 $aA beginner's guide to image shape feature extraction techniques$94490023 997 $aUNINA