Pattern recognition

Classical

Mean-Square Regression

=Objective= Very fast if you already have a list of pixels you know belong to one contour, and you want to check if it fits to a parametric shape. =Quick Def= =References= =Full Definition=

Hough Transforms

Standard Hough Transform
Randomized Hough Transform

[Xu,Oja,Kultanen,1989] =Objective= Improve speed, resolution, low memory needs, infinite scale. =Quick Def= If n parameters, take n points and only accumulate one point. =References=

=Full Definition=

Connective Randomized Hough Transform

[Kalvianen,Hirvonen] =Objective= =Quick Def= =References=

=Full Definition=

Combinatorial Hough Transform

=Objective= =Quick Def= =References= =Full Definition=

Adaptive Hough Transform

[Ilingworth,Kittler,1986] =Objective= Improve speed and resolution. =Quick Def= First time at low resolution, then second time at higher resolution where there are peaks. =References= =Full Definition=

Probabilistic Hough Transform

[Kiryati,Eldar,Bruckshtein,1990] =Objective= Improve speed. =Quick Def= Only process n% of the pixels. =References= =Full Definition=

Adaptive Probabilistic Hough Transform

=Objective= =Quick Def= =References= =Full Definition=

Progressive Probabilistic Hough Transform

=Objective= =Quick Def= =References= =Full Definition=

Hierarchical Hough Transform

[Princen,1989] =Objective= =Quick Def= =References= =Full Definition=

Sampling Hough Transform

=Objective= =Quick Def= =References=

=Full Definition=

Generalized Hough Transform

=Objective= =Quick Def= =References= =Full Definition=

UpWrite method

Learning

ai/methods/pattern-recognition.txt · Last modified: 2013/09/19 16:40 (external edit)
 
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