Abstract
We propose an extension of the three-point Randomized Hough transform. Our new Hough transform, which permits a continuous voting space without any cell-tessellation, uses both one-to-one mapping from an image plane to the parameter space and from the parameter space to the image plane. These transforms define a parameter from samples and a line from a parameter, respectively. Furthermore, we describe the classical Hough transform, the randomized Hough transform, the three-point randomized Hough transform and our new Hough transform in a generalized framework using geometric duality.
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© 2006 Springer-Verlag Berlin Heidelberg
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Mochizuki, Y., Torii, A., Imiya, A. (2006). N-Point Hough Transform Derived by Geometric Duality. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_15
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DOI: https://doi.org/10.1007/11949534_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
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