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The Hough Transform's Implicit Bayesian Foundation
2007
2007 IEEE International Conference on Image Processing
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.
doi:10.1109/icip.2007.4380033
dblp:conf/icip/TorontoMVS07
fatcat:su7inlkrk5hupchbx4srvljm5q