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Proceedings Paper

Corner detection using spline wavelets
Author(s): Andrew K. Chan; Charles K. Chui; Jun Zha; Q. Liu
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Paper Abstract

Detection of corners in an image is very useful in computer vision and pattern recognition. The existing algorithms for corner detection seem to be insufficient in many situations. The corner detection algorithm proposed in this paper is based on spline-wavelet decompositions. Corner and edge detectors are constructed from the 2-D wavelet transform coefficients. A somewhat sophisticated thresholding technique is applied to remove noise and minor irregularities in the images. Noise can be further reduced if additional processing is applied to the component images at all resolutions. Information on the edges and corners is contained in the component images in all the octaves to facilitate precise localization. A real-time wavelet decomposition algorithm is developed for the corner and edge detectors. It is very efficient and requires very little memory, since most of the computations involve only simple moving average operations and sub-sampling.

Paper Details

Date Published: 1 February 1992
PDF: 12 pages
Proc. SPIE 1610, Curves and Surfaces in Computer Vision and Graphics II, (1 February 1992); doi: 10.1117/12.135154
Show Author Affiliations
Andrew K. Chan, Texas A&M Univ. (United States)
Charles K. Chui, Texas A&M Univ. (United States)
Jun Zha, Texas A&M Univ. (United States)
Q. Liu, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 1610:
Curves and Surfaces in Computer Vision and Graphics II
Martine J. Silbermann; Hemant D. Tagare, Editor(s)

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