Generic computer vision methods

  1. General image segmentation methods
    1. Clustering methods
    2. Compression-based methods
    3. Histogram-based methods
    4. Region-growing methods
    5. Split-and-merge methods
    6. Partial differential equation-based methods
    7. Graph partitioning methods
    8. Multi-scale segmentation
    9. Semi-automatic segmentation
    10. Trainable segmentation
    11. Segmentation benchmarking
  2. Accumulation/voting methods
    1. Hough transform
      1. Adaptive Hough Transform
      2. Hough transform of curves
      3. Cascaded Hough Transform
      4. Generalised Hough transform
      5. Hierarchical Hough Transform
      6. Maximum margin Hough transform
      7. Probabilistic Hough Transform
      8. Randomised Hough Transform
      9. Surface finding
    2. Tensor Voting
  3. Diffusion/PDE/Time based evolution methods
    1. Heat kernel -- see also scale-space which is based on linear diffusion and the Gaussian (heat) kernel
  4. Eigendecompositions
  5. Genetic algorithms/Genetic programming
  6. Graph Methods
    1. Graph representations
      1. Adjacency graph
      2. Association graph
      3. Attributed Graph
      4. Dynamic Feature Graph
      5. Graph embedding
      6. Hierarchical graph/Hypergraph representations
      7. Laplacian smoothing
      8. Median graph
      9. Optimal Basis Graphs
      10. Probabilistic graphical model, Probabilistic graph theory
    2. Graph matching
      1. Bayesian Graph Matching
      2. Bipartite matching
      3. Graph cuts
      4. Graph kernel methods
      5. Graph edit distance
      6. Maximal cliques in Association graphs
      7. Spectral decomposition methods
      8. Subgraph isomorphism problem
    3. Multidimensional scaling
  7. Image pyramids and scale reduction
    1. Adaptive Pyramids
    2. Gaussian pyramids
    3. Laplacian pyramids
  8. Level sets
    1. Level set trees
  9. Minimum description length
  10. Multiple Scales/Resolutions
    1. Multiple-scale analysis
      1. Multi-Scale Integration
    2. Fractals
    3. Ranklets
    4. Scale space
    5. Wavelets
      1. Noiselets
  11. Graph, networks and connectionist methods
    1. Bayesian networks
    2. Connectionist methods
    3. Gaussian processes methods
    4. Neural networks
    5. Probabilistic graphical models
      1. Expectation propagation
      2. Belief propagation
      3. Message passing
        1. Variational message passing
        2. Tree reweighted message passing
    6. Radial basis function networks
    7. Wavelet Networks
  12. Regularization
  13. Relaxation
    1. Continuous
    2. Discrete
    3. Probabilistic/Stochastic
    4. Linear programming relaxation
    5. Lagrangian relaxation
  14. Spatial indexing/hashing
  15. Subpixel Methods
  16. Super-resolution
  17. Certainty/uncertainty representations
    1. Bayesian networks
    2. Discrete (See Relaxation)
    3. Fuzzy logic
    4. Intervals
    5. Probabilities
  18. Vision paradigms
    1. Active vision
    2. Geometric vision (See Vision Geometry and Mathematics and Geometric Representation of Model Features)
    3. Purposive Vision
    4. Qualitative Vision
  19. Vision system design and characterization
    1. Propagation of uncertainty
    2. Performance testing in vision
    3. Receiver operating characteristic
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