Object Recognition

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.