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Table of Contents
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
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=
