13 Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants Table of Contents (Back)
Keyword list at end

• 13.1 Matching, Volumes, 3-D
  • 13.1.1 Aspect Graph Matching, Characteristic Views
    • 13.1.1.1 Aspect Graphs, Matching Systems
    • 13.1.1.2 Aspect Graph Matching -- Bowyer
    • 13.1.1.3 Aspect Graph Matching -- Ikeuchi
  • 13.1.2 3-D Object Recognition Using Invariants
    • 13.1.2.1 Invariants, General Issues and Techniques
    • 13.1.2.2 Invariants, Points
    • 13.1.2.3 Invariants, Lines, Curves
    • 13.1.2.4 Invariants, Areas
    • 13.1.2.5 Invariants, Projective, Perspective
    • 13.1.2.6 Affine Invariants
    • 13.1.2.7 Invariance Papers -- Mundy
  • 13.1.3 Matching Using Accumulation and Alignment Schemes
    • 13.1.3.1 Pose Estimation, 3D Models
    • 13.1.3.2 Grimson Object Recognition Papers
• 13.2 Combined Feature Matching
• 13.3 Graph Matching and Relaxation
  • 13.3.1 Graph Matching Theoretical Issues
  • 13.3.2 General Structure and Graph Representation and Matching
  • 13.3.3 Matching Graphs and 3-D Network Descriptions
  • 13.3.4 Graph Matching, Neural Networks, Hopfield Networks
  • 13.3.5 Graph Matching, Continuous Relaxation
    • 13.3.5.1 Continuous Relaxation Theory
    • 13.3.5.2 Boltzmann Machine, Simulated Annealing, and Related Topics
    • 13.3.5.3 Hummel and Zucker Relaxation Papers
    • 13.3.5.4 Shmuel Peleg Theoretical Relaxation Papers
    • 13.3.5.5 Faugeras and Berthod Gradient Optimization Methods
  • 13.3.6 Discrete Relaxation Methods
    • 13.3.6.1 Discrete Relaxation Theoretical Issues
  • 13.3.7 Matching Using "Tree" Searching Techniques
    • 13.3.7.1 Tabu Search
  • 13.3.8 Basic Comparison of Relational Network Descriptions
    • 13.3.8.1 Evidence Theory, Combination Techniques, Optimization Techniques
      • 13.3.8.1.1 Fuzzy Sets, Fuzzy Logic
    • 13.3.8.2 Maximum Likelihood Estimation, Classification
    • 13.3.8.3 Energy Minimization, Energy Maximization Computation, Function Solving, Optimizations
    • 13.3.8.4 Bayesian Networks, Bayes Nets
    • 13.3.8.5 Graph Edit Distance, Tree-Edit Distance
    • 13.3.8.6 Linear Prediction Techniques
  • 13.3.9 Other Structural Matching
• 13.4 Object Recognition, General Techniques
  • 13.4.1 Invariants -- Eigen Representations, General Appearance Based Methods
    • 13.4.1.1 Active Appearance Models - AAM
    • 13.4.1.2 Invariants -- Principal Component Analysis
    • 13.4.1.3 Invariants -- ICA, Independent Component Analysis
    • 13.4.1.4 Invariants -- Linear Discriminant Analysis, Fisher Linear Discriminant
    • 13.4.1.5 Surveys, Comparisons, Evaluations, Principal Components
    • 13.4.1.6 Learning for Principal Components, Eigen Representations
    • 13.4.1.7 Computation and Analysis of Principal Components, Eigen Values, SVD
    • 13.4.1.8 Faces using Invariants -- Eigenfaces
      • 13.4.1.8.1 ICA, PCA in Face Recognition
      • 13.4.1.8.2 LDA in Face Recognition
  • 13.4.2 Three-Dimensional Matching Using Hashing/Indexing
  • 13.4.3 Model Based Recognition Systems
    • 13.4.3.1 Parameterized Models, Fit Model to Objects
    • 13.4.3.2 Associative Memory for Matching and Recognition
  • 13.4.4 3-D Object Recognition from Pose Estimation or Alignment
  • 13.4.5 Object Recognition Evaluation
• 13.5 General Spatial Reasoning and Geometric Reasoning Issues
• 13.6 Knowledge-Based Vision
  • 13.6.1 General Issues -- Knowledge-Based Vision
    • 13.6.1.1 Knowledge-Based Vision, Surveys, Overviews
  • 13.6.2 ACRONYM and SUCCESSOR Papers - Stanford University and Others
  • 13.6.3 University of Massachusetts VISIONS System
  • 13.6.4 Constraint Based Matching
  • 13.6.5 Rule-Based or Expert Systems for Recognition
  • 13.6.6 Recognition by Function
  • 13.6.7 Integration of Vision Modules
  • 13.6.8 Context in Computer Vision
    • 13.6.8.1 Context Supplied by Text or Language
    • 13.6.8.2 Context from the environment
• 13.7 General References
  • 13.7.1 String Matching
• 13.8 Other Feature Matching Techniques