TY - GEN
T1 - Fourier parameterization provide uniform bounded Hough Space
AU - Lam, Wilson C. Y.
AU - Yuen, Kelvin S. Y.
AU - Leung, Dennis N. K.
PY - 1993/9
Y1 - 1993/9
N2 - The Hough transform is a popular technique for feature detection and object recognition. In its original formulation for lines, the slope parameter is unbounded. Moreover, a rotation of the image will cause uneven representation accuracy of the slope. In a classic paper, Duda and Hart solved these problems by introducing a modified ρ-θ parameterization, which has since become very popular in the computer vision community for parameterizing lines. Unfortunately, no equivalent parameterization exists for curves. This has led to ad hoc choice of parameterizations for circles, ellipses, conic sections etc. In this paper, we propose the novel use of Fourier descriptor as parameterized curve equation in a Hough transform. We show that using this Fourier parameterization, all parameters are bounded and the representation have uniform accuracy for all parameters.
AB - The Hough transform is a popular technique for feature detection and object recognition. In its original formulation for lines, the slope parameter is unbounded. Moreover, a rotation of the image will cause uneven representation accuracy of the slope. In a classic paper, Duda and Hart solved these problems by introducing a modified ρ-θ parameterization, which has since become very popular in the computer vision community for parameterizing lines. Unfortunately, no equivalent parameterization exists for curves. This has led to ad hoc choice of parameterizations for circles, ellipses, conic sections etc. In this paper, we propose the novel use of Fourier descriptor as parameterized curve equation in a Hough transform. We show that using this Fourier parameterization, all parameters are bounded and the representation have uniform accuracy for all parameters.
UR - https://www.scopus.com/pages/publications/84898787569
UR - https://www.scopus.com/pages/publications/84898787569#tab=impact
U2 - 10.1007/3-540-57233-3_25
DO - 10.1007/3-540-57233-3_25
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-3-540-57233-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 183
EP - 190
BT - Computer Analysis of Images and Patterns
A2 - Chetverikov, Dmitry
A2 - Kropatsch, Walter
PB - Springer Berlin Heidelberg
T2 - 5th International Conference on Computer Analysis of Images and Patterns, CAIP 1993
Y2 - 13 September 1993 through 15 September 1993
ER -