A Dynamics of the Hough Transform and Artifical Neural Networks

Authors:
Atsushi Imiya;Kazuhiko Kawamoto
Affiliations:
-;-
Venue:
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Year:
1999

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Abstract

The least-squares method efficiently solves the model fitting problems, if we assume model equations. However, to the model fitting for a collection of models, the classification of data is required as preprocessing. We show that the randomized Hough transform achieves both the model fitting by the least-squares method and the classification of sample points by permutation simultaneously. Furthermore, we derive a dynamical system for the line detection by the Hough transform, which achieves grouping of sample points as the permutation of data sequence. The theoretical analysis in this paper verifies the reliability of the Hough-transform based template matching for the detection of shapes from a scene.