Recognition and registration methods

A part of the CVonline computer vision resource summarizing different approaches to structure registration and classification as commonly used in computer vision and image processing.
  1. Statistical classification methods
    1. Bayesian classifier
    2. Contextual image classification
    3. Decision trees
    4. Learning classifier systems
    5. Feature-based
    6. Fuzzy classification
    7. Hough forest
    8. k-nearest neighbor classification
    9. Linear and higher order discriminant functions
    10. Markov random field based classification
    11. Minimum distance estimation
    12. Multi-classifier fusion
    13. Neural networks
    14. Random Forest
    15. Sparse coding
    16. Support vector machines and two-sided SVMs
    17. Vector quantization based classification
  2. General reasoning methods used in vision
    1. Ambiguous images
  3. Geometric model matching, Feature correspondence, Shape correspondence
    1. General matching control and search algorithms
      1. Hypothesize and test
      2. Heuristic search
      3. Recognition by components
      4. Interpretation trees and other search tree variations
      5. Rule-based systems
      6. Theorem proving
      7. RAST algorithms
    2. General recognition methods
      1. Appearance-based, Iconic, View-based recognition
      2. Boltzmann machine, Hopfield net, Simulated annealing
      3. Constraint-based matching
      4. Context-based matching
      5. Template matching
        1. Deformable/elastic template matching
        2. Pictorial structures
        3. Sub-template matching
      6. Geometric hashing
      7. Image stitching
      8. Image registration
      9. Inverse compositional method
      10. Iterated closest point/Iterative closest point (ICP)
        1. Non-Rigid ICP
      11. MAPSAC
      12. Model based recognition
      13. Multi-scale contour segmentation
      14. Non-rigid alignment (See also Introduction)
      15. Object categorization representation
        1. Cognitive neuroscience of visual object recognition
        2. Bag of words
        3. Fisher Vector
        4. Structured models
        5. Subcategory recognition
        6. Vocabulary trees
      16. Object Size/Scale/distance estimation
      17. Polygon matching
      18. Pose clustering
      19. RANSAC
      20. Relaxation labeling
      21. Softassign algorithm
      22. Structural description
      23. Template and cross-correlation matching
      24. View-based object recognition 
  4. Identity verification/alignment
    1. Geometric feature proximity
    2. Pose consistency
  5. Model based indexing, invocation
  6. Special feature matching
    1. 2D to 2D point feature matching
      1. Proximity matrices
    2. 2D to 3D point feature matching
    3. 3D to 3D point feature matching
    4. 2D, 3D point to structure matching
    5. Aspect graph matching
    6. Boundary/contour/curve/edge matching
      1. TERCOM
    7. Line matching
    8. Phase matching
    9. Property-based matching
    10. Optical/Appearance flow matching
    11. Polygon matching
    12. Needle-map matching
    13. Region matching
    14. Spatial pyramid matching
    15. Surface matching
    16. Texture classification
    17. Texture matching
    18. Volume matching
  7. Syntactic pattern matching
    1. 1D/String matching
    2. 2D/Pattern grammars
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