The γ–ω hough transform: Linearizing voting curves in an unbiased ϱ–θ parameter space
✍ Scribed by Toshikazu Wada; Takahiro Fujii; Takashi Matsuyama
- Book ID
- 104591578
- Publisher
- John Wiley and Sons
- Year
- 1993
- Tongue
- English
- Weight
- 872 KB
- Volume
- 24
- Category
- Article
- ISSN
- 0882-1666
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✦ Synopsis
Abstract
The Hough transform is an effective method for detecting figure elements in images corrupted by noise. Voting on the feature points in the image space is performed in the parameter space, after which the points in the parameter space with many votes are focused on in order to detect the figure elements. In the Hough transform, the parameter space is partitioned into elements called cells where the votes are accumulated. However, when the subject is a digital image, if the parameter space is not sampled appropriately, a bias arises in the number of votes accumulated in the cells. In this paper, we present a sampling method for the parameter space that does not produce distortion in the ρ–θ parameter space used in line detection, and we construct a γ–ω space where no bias in the number of votes arises even when sampling is uniform. The γ–ω parameter space corresponds to the ρ–θ parameter space and has the features of no bias in the number of votes and voting loci, which are segmented lines. In addition, the characteristics of the γ–ω parameter space, where the feature points and voting loci are easily converted from one to the other, are used, and a verification method for the line segments is proposed that does not scan the image space again. By combining the line detection method that uses the γ–ω parameter space and this verification method, stable line detection becomes possible.