Object Recognition - Bennamoun, M.; Mamic, G. J.
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This volume introduces the fundamental concepts and tools involved in the design and implementation of object recognition systems. Divided into three parts, it first introduces the topic and covers the acquisition of images, then details 3-D object reconstruction, modelling and matching, and finally describes typical recognition systems using case studies. Key features include: Extensive literature surveys of state-of-the-art systems Recognition will be essential reading for research scientists, advanced undergraduate and postgraduate students in computer vision, image processing and pattern…mehr

Produktbeschreibung
This volume introduces the fundamental concepts and tools involved in the design and implementation of object recognition systems. Divided into three parts, it first introduces the topic and covers the acquisition of images, then details 3-D object reconstruction, modelling and matching, and finally describes typical recognition systems using case studies. Key features include: Extensive literature surveys of state-of-the-art systems Recognition will be essential reading for research scientists, advanced undergraduate and postgraduate students in computer vision, image processing and pattern classification. It will also be of interest to practitioners working in the field of computer vision.
  • Produktdetails
  • Advances in Pattern Recognition
  • Verlag: Springer, Berlin
  • Erscheinungstermin: Januar 2002
  • Englisch
  • Abmessung: 243mm x 162mm x 27mm
  • Gewicht: 760g
  • ISBN-13: 9781852333980
  • ISBN-10: 1852333987
  • Artikelnr.: 22241698
Autorenporträt
Mohammed Bennamoun, Queensland University of Technology, Brisbane, QLD, Australia / George Mamic, Queensland University of Technology, Brisbane, QLD, Australia
Inhaltsangabe
A
Introduction and Acquisition Systems.
1. Introduction.
1.1 What Is Computer Vision?.
1.2 Background and History.
1.3 Classification of Existing Vision Systems.
1.3.1 Marr's Theory.
1.3.2 Model
based Object Recognition.
1.4 Problem Formulation.
1.4.1 Mathematical Formulation.
1.5 Why Is Automatic Object Recognition a Difficult Problem?.
1.6 Motivations and Significance.
1.6.1 Industry.
1.6.2 Community.
1.7 A 2
D System or a 3
D System?.
1.8 Specifications / Themes of Interest in Object Recognition.
1.9 Acquisition Systems.
1.9.1 Intensity Images.
1.9.2 Range Imaging Technologies.
1.9.3 Miscellaneous Modalities.
1.10 Taxonomy.
2. Stereo Matching and Reconstruction of a Depth Map.
2.1 Fundamentals of Stereo Vision.
2.1.1 Stereo Vision Paradigm.
2.1.2 Image Matching.
2.1.3 Matching Problems.
2.2 Review of Existing Techniques.
2.3 Area
based Techniques.
2.3.1 Simple Matching Measures.
2.3.2 Validation Techniques.
2.3.3 Hierarchical Methods.
2.3.4 Adaptive Window Techniques.
2.3.5 Sparse Point Matching.
2.3.6 Dense Matching.
2.3.7 Symmetric Multi
Window Technique.
2.3.8 Unmanned Ground Vehicle Implementation.
2.3.9 Multiple Baseline Techniques.
2.3.10 Least Squares Matching.
2.4 Transform
based Techniques.
2.4.1 Sign Representation.
2.4.2 Non
parametric Techniques.
2.5 Symbolic Feature
based Techniques.
2.5.1 Zero Crossing Matching.
2.5.2 Edge Matching.
2.5.3 Patch Matching.
2.5.4 Relational Matching.
2.6 Hybrid Techniques.
2.6.1 Cross Correlation Combined with Edge Information.
2.7 Phase
based Techniques.
2.8 Combining Independent Measurements.
2.9 Relaxation Techniques.
2.9.1 Cooperative Algorithm.
2.9.2 Relaxation Labelling.
2.10 Dynamic Programming.
2.10.1 Viterbi Algorithm.
2.10.2 Intra
and Inter
Scanline Search.
2.10.3 Disparity Space Image.
2.11 Object Space Techniques.
2.11.1 Combining Matching and Surface Reconstruction.
2.11.2 Object Space Models.
2.12 Existing Matching Constraints and Diagnostics.
2.12.1 Matching Constraints.
2.12.2 Matching Diagnostics.
2.12.3 Discussion.
2.13 Conclusions.
A
Summary.
B
Database Creation and Modelling for 3
D Object Recognition.
3. 3
D Object Creation for Recognition.
3.1 Preliminaries of 3
D Registration.
3.2 Registration Paradigm.
3.2.1 General Specifications.
3.3 Chronological Literature Review.
3.4 Fundamental Techniques.
3.4.1 Registration with Point Correspondences.
3.4.2 Registration Without Correspondences.
3.5 Uncertainty in 3
D Registration.
3.5.1 Weighted Correspondences.
3.5.2 A Better Approach.
3.6 Simultaneous Multiple View Registration.
3.6.1 Simple Approaches.
3.6.2 Rigid Body Modelling.
3.6.3 Multiple View Chen and Medioni.
3.7 View Integration and Surface Reconstruction.
3.7.1 Integration versus Reconstruction.
3.7.2 Volumetric Integration Methods.
3.7.3 Volumetric Reconstruction.
3.7.4 Geometric Integration Methods.
3.7.5 Geometric Reconstruction.
3.8 Registration
Case Study.
3.8.1 Notation and Terminology.
3.8.2 Problem Reformulation.
3.8.3 Iterative Algorithm to Solve for R.
3.8.4 Results.
3.8.5 Conclusions.
3.9 Surface Reconstruction Summary.
4. Object Representation and Feature Matching.
4.1 Preliminaries.
4.2 Object
centred Representations.
4.2.1 Boundary and Curve
based Representations.
4.2.2 Axial Descriptions.
4.2.3 Surface Descriptions.
4.2.4 Volumetric Descriptions.
4.3 Viewer
centred Representations.
4.3.1 Aspect Graphs.
4.3.2 Silhouettes.
4.3.3 Principal Component Analysis.
4.3.4 Miscellaneous Techniques.
4.4 Representation Conclusions.
4.5 Matching.
4.5.1 Hypothesise and Test.
4.5.2 Relational Structures.
4.5.3 Pose Clustering.
4.5.4 Geometric Hashing.
4.5.5 Interpretation Trees.
4.5.6 Registration and Distance Transforms.
4.6 Matching Conclusions.
B
Summary.
C
Vision Systems
Case Studies.
5. Optical Character Recognition.
5.1 Examples of Existing Systems.
5.1.1 Prototype Extraction and Adaptive OCR.
5.1.2 Direct Grayscale Extraction of Features for Character Recognition.
5.2 Optical Character Recognition System for Cursive Scripts
A Case Study.
5.2.1 Background.
5.2.2 An Overview of the Case Study System.
5.2.3 The Document Image Analysis Step.
5.2.4 The Recognition
based Segmentation Step.
5.2.5 The Feature Extraction Stage.
5.2.6 Results.
5.2.7 Conclusions.
6. Recognition by Parts and Part Segmentation Techniques.
6.1 Examples of Existing Vision Systems.
6.1.1 HYPER.
6.1.2 The Logarithmic Complexity Matching Technique.
6.2 Recognition by Parts and Part Segmentation
A Case Study.
6.2.1 The Edge Detection Stage.
6.2.2 The Part Segmentation Stage.
6.2.3 Part Isolation.
6.2.4 The Part Identification Stage.
6.2.5 The Structural Description and Recognition Stage.
6.2.6 Results.
6.2.7 Discussion.
6.2.8 Conclusions.
7. 3
D Object Recognition Systems.
7.1 Examples of Existing Systems.
7.1.1 ACRONYM.
7.1.2 SCERPO.
7.1.3 3DPO.
7.1.4 PREMIO.
7.1.5 Recognition of MSTAR Targets.
7.1.6 Bayesian Recognition by Parts in FLIR.
7.2 3
D Free
form Object Recognition Using Bayesian Splines A Case Study.
7.2.1 Preliminaries.
7.2.2 Bayesian Formulation.
7.2.3 The RJMCMC Algorithm for Splines.
7.2.4 Simulated Annealing RJMCMC.
7.2.5 Matching Splines.
7.2.6 Results.
7.2.7 Conclusions.
C
Summary.
Appendices.
A. Vector and Matrix Analysis.
A.1 Preliminaries.
A.1.1 Determinant.
A.1.2 Inversion.
A.2 Derivatives and Integrals of Matrices.
A.3 Vectors and Vector Analysis.
A.4 Eigenvalues and Eigenvectors.
A.5 Quadratic Forms.
B. Principal Component Analysis.
C. Optimisation Fundamentals.
C.1 Fundamental Concepts.
C.2 Linear Least Squares.
C.3 Non
linear Optimisation.
C.4 Direct Search Techniques.
C.4.1 Simplex Method.
C.5 Gradient Methods.
C.5.1 Newton
Raphson Technique.
C.5.2 Davidon
Fletcher
Powell.
C.6 Simulated Annealing.
D. Differential Geometry
Basic Principles.
E. Spline Theory.
E.1 Spline Definitions.
F. Detailed Derivation of Registration Equations.
References.

A - Introduction and Acquisition Systems.- 1. Introduction.- 1.1 What Is Computer Vision?.- 1.2 Background and History.- 1.3 Classification of Existing Vision Systems.- 1.3.1 Marr's Theory.- 1.3.2 Model-based Object Recognition.- 1.4 Problem Formulation.- 1.4.1 Mathematical Formulation.- 1.5 Why Is Automatic Object Recognition a Difficult Problem?.- 1.6 Motivations and Significance.- 1.6.1 Industry.- 1.6.2 Community.- 1.7 A 2-D System or a 3-D System?.- 1.8 Specifications / Themes of Interest in Object Recognition.- 1.9 Acquisition Systems.- 1.9.1 Intensity Images.- 1.9.2 Range Imaging Technologies.- 1.9.3 Miscellaneous Modalities.- 1.10 Taxonomy.- 2. Stereo Matching and Reconstruction of a Depth Map.- 2.1 Fundamentals of Stereo Vision.- 2.1.1 Stereo Vision Paradigm.- 2.1.2 Image Matching.- 2.1.3 Matching Problems.- 2.2 Review of Existing Techniques.- 2.3 Area-based Techniques.- 2.3.1 Simple Matching Measures.- 2.3.2 Validation Techniques.- 2.3.3 Hierarchical Methods.- 2.3.4 Adaptive Window Techniques.- 2.3.5 Sparse Point Matching.- 2.3.6 Dense Matching.- 2.3.7 Symmetric Multi-Window Technique.- 2.3.8 Unmanned Ground Vehicle Implementation.- 2.3.9 Multiple Baseline Techniques.- 2.3.10 Least Squares Matching.- 2.4 Transform-based Techniques.- 2.4.1 Sign Representation.- 2.4.2 Non-parametric Techniques.- 2.5 Symbolic Feature-based Techniques.- 2.5.1 Zero Crossing Matching.- 2.5.2 Edge Matching.- 2.5.3 Patch Matching.- 2.5.4 Relational Matching.- 2.6 Hybrid Techniques.- 2.6.1 Cross Correlation Combined with Edge Information.- 2.7 Phase-based Techniques.- 2.8 Combining Independent Measurements.- 2.9 Relaxation Techniques.- 2.9.1 Cooperative Algorithm.- 2.9.2 Relaxation Labelling.- 2.10 Dynamic Programming.- 2.10.1 Viterbi Algorithm.- 2.10.2 Intra- and Inter-Scanline Search.- 2.10.3 Disparity Space Image.- 2.11 Object Space Techniques.- 2.11.1 Combining Matching and Surface Reconstruction.- 2.11.2 Object Space Models.- 2.12 Existing Matching Constraints and Diagnostics.- 2.12.1 Matching Constraints.- 2.12.2 Matching Diagnostics.- 2.12.3 Discussion.- 2.13 Conclusions.- A - Summary.- B - Database Creation and Modelling for 3-D Object Recognition.- 3. 3-D Object Creation for Recognition.- 3.1 Preliminaries of 3-D Registration.- 3.2 Registration Paradigm.- 3.2.1 General Specifications.- 3.3 Chronological Literature Review.- 3.4 Fundamental Techniques.- 3.4.1 Registration with Point Correspondences.- 3.4.2 Registration Without Correspondences.- 3.5 Uncertainty in 3-D Registration.- 3.5.1 Weighted Correspondences.- 3.5.2 A Better Approach.- 3.6 Simultaneous Multiple View Registration.- 3.6.1 Simple Approaches.- 3.6.2 Rigid Body Modelling.- 3.6.3 Multiple View Chen and Medioni.- 3.7 View Integration and Surface Reconstruction.- 3.7.1 Integration versus Reconstruction.- 3.7.2 Volumetric Integration Methods.- 3.7.3 Volumetric Reconstruction.- 3.7.4 Geometric Integration Methods.- 3.7.5 Geometric Reconstruction.- 3.8 Registration - Case Study.- 3.8.1 Notation and Terminology.- 3.8.2 Problem Reformulation.- 3.8.3 Iterative Algorithm to Solve for R.- 3.8.4 Results.- 3.8.5 Conclusions.- 3.9 Surface Reconstruction Summary.- 4. Object Representation and Feature Matching.- 4.1 Preliminaries.- 4.2 Object-centred Representations.- 4.2.1 Boundary and Curve-based Representations.- 4.2.2 Axial Descriptions.- 4.2.3 Surface Descriptions.- 4.2.4 Volumetric Descriptions.- 4.3 Viewer-centred Representations.- 4.3.1 Aspect Graphs.- 4.3.2 Silhouettes.- 4.3.3 Principal Component Analysis.- 4.3.4 Miscellaneous Techniques.- 4.4 Representation Conclusions.- 4.5 Matching.- 4.5.1 Hypothesise and Test.- 4.5.2 Relational Structures.- 4.5.3 Pose Clustering.- 4.5.4 Geometric Hashing.- 4.5.5 Interpretation Trees.- 4.5.6 Registration and Distance Transforms.- 4.6 Matching Conclusions.- B - Summary.- C - Vision Systems - Case Studies.- 5. Optical Character Recognition.- 5.1 Examples of Existing Systems.- 5.1.1 Prototype Extraction and Adaptive OCR.- 5.1.2 Direct Grays