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.
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© 1999 Springer-Verlag Berlin Heidelberg
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Imiya, A., Kawamoto, K. (1999). A Dynamics of the Hough Transform and Artificial Neural Networks. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_4
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DOI: https://doi.org/10.1007/3-540-48097-8_4
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