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CVonline Subject Linkages
Databases and indexing related concepts
Generic computer vision methods
Geometric and other image features and methods
Geometry and mathematics
Image physics related concepts
Image Processing Architectures & Control Structures
Image transformations and filters
Introductory visual neurophysiology
Introductory visual psychophysics/psychology
Motion and time sequence analysis related concepts
Non-sequential realization methods
Object, world and scene representations
Recognition and registration methods
Scene understanding/image analysis methods
Sensor fusion, registration and planning methods
Sensors and Properties
System models, calibration and parameter estimation methods
Visual learning related methods and concepts
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Generic computer vision methods
A part of the
CVonline
computer vision resource listing methods that are used widely across cmputer vision and image processing.
General
image se
gmentation
methods
Clustering methods
Compression-based methods
Histo
gram
-based methods
Region-growing methods
Split-and-merge methods
Partial differential equation-bas
ed
methods
Graph part
itioning
methods
Multi-s
cale
segmentation
Semi-automatic segmentation
Trainable segmentation
Segmentation benchmarking
Accumulation/voting methods
Hough transform
Adaptive Hough Transform
Hough transform of curves
Cascaded Hough Transform
Generalised Hough transform
Hierarchical Hough Transform
Maximum margin
Hough transform
Probabilistic Hough Transform
Ra
ndomised Hough Transform
Surface finding
T
ensor
Voting
Diffusion
/
PDE
/Time based evolution methods
Heat kernel
-- see also
scale-space
which is based on linear diffusion and the Gaussian (heat) kernel
Eigendecompositions
Genetic algorithms
/
Genetic programming
Graph Methods
Graph representations
Adjacency graph
Association graph
Attributed Graph
Dynamic Feature Graph
Graph embedding
Hierarchical graph/Hypergraph representations
Laplacian smoothing
Median graph
Optimal Basis Graphs
Probabilistic graphical model
,
Probabilistic graph theory
Graph matching
Bayesian Graph Matching
Bipartite matching
Graph cuts
Graph
kernel methods
Graph
edit distance
Maximal cliques
in
Association graphs
Spectral decomposition
methods
Subgraph isomorphism problem
Multidimensional scaling
Image pyramids and scale reduction
Adaptive Pyramids
Gaussian pyramids
Laplacian pyramids
Level sets
Level set
trees
Matching methods
Hungarian algorithm
Minimum description length
Model Selection Criteria
Akaike Information Criterion
Bayesian Information Criterion
Multiple Scales/Resolutions
Multiple-sca
le analysis
Multi-Scale Integration
Fractals
Ranklets
Scale space
Wavelets
Noiselets
Graph, networks and connectionist methods
Bayesian networks
Connectionist methods
Gaussian processes
methods
Neural networks
Probabilistic graphical models
Expectation propagation
B
elief propagation
Message passing
Variational message passing
Tree reweighted message passing
Radial basis function networks
W
avelet
Networks
Regularization
Relaxation
Continuous
Discrete
Probabilistic/Stochastic
Linear programming relaxation
Lagrangian relaxation
Spatial indexing/hashing
Su
bpixel
Methods
Su
per-resolution
Certainty/uncertainty representations
Bayesian networks
Discrete (See
Relaxation
)
Fuzzy logic
Intervals
Probabilities
Vision paradigms
Active vision
Geometric vision (
See
Vision Geometry and Mathematics
and
Geometric Representation of Model Features
)
Purposive Vision
Qualitative Vision
Vision system design and characterization
Bounding box overlap assessment:
IoU: Intersection over Union
MABO: Mean Average Best Overlap
Propagation of uncertainty
Performance testing in vision
Receiver operating characteristic
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