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Proceedings Paper

Nonparametric dominant point detection
Author(s): Nirwan Ansari; KuoWei Huang
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Paper Abstract

A new method for detecting dominant points is presented. It does not require any input parameter, and the dominant points obtained by this method remain relatively the same even when the object curve is scaled or rotated. In this method, for each boundary point, a support region is assigned to the point based on its local properties. Each point is then smoothed by a Gaussian filter with a width proportional to its determined support region. A significance measure for each point is then compared. Dominant points are finally obtained through nonmaximum suppression. Unlike other dominant point detection algorithms which are sensitive to scaling and rotation of the object curve, the new method will overcome this difficulty. Furthermore, it is robust in the presence of noise. The proposed new method is compared to a well-known dominant point detection algorithm in terms of the computational complexity and the approximation errors.

Paper Details

Date Published: 1 November 1991
PDF: 12 pages
Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50401
Show Author Affiliations
Nirwan Ansari, New Jersey Institute of Technology (United States)
KuoWei Huang, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1606:
Visual Communications and Image Processing '91: Image Processing
Kou-Hu Tzou; Toshio Koga, Editor(s)

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