The Hough Transform's Implicit Bayesian Foundation

This paper shows that the basic Hough transform is implicitly a Bayesian process--that it computes an unnormalized posterior distribution over the parameters of a single shape given feature points. The proof motivates a purely Bayesian approach to the problem of finding parameterized shapes in digital images. A proof-of-concept implementation that finds multiple shapes of four parameters is presented. Extensions to the basic model that are made more obvious by the presented reformulation are discussed.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Neil Toronto, Bryan S. Morse, Dan Ventura, Kevin D. Seppi
• Keywords: Basic Hough Transform, Bayesian Approach, ICIP 2007, Image Processing, Parameterized Shapes, Shape Given Feature, Unnormalized Posterior Distribution