Abstract
A new feature of curves pertaining to the acceptance/rejection decision in curve detection is proposed. The feature measures a curve's distinctiveness in its neighborhood, which is modeled by a one-parameter family of curves. A computational framework based on the Hough transform for extracting the distinctiveness feature is elaborated and examples of feature extractors for the circle and the ellipse are given. It is shown that the proposed feature can be extracted efficiently and is effective in separating signals from false positives. Experimental results with circle and ellipse testing that strongly support the efficiency and effectiveness claims are obtained. The results further demonstrate that the proposed feature exhibits good noise resiliency.
| Original language | English |
|---|---|
| Pages (from-to) | 1215-1222 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 28 |
| Issue number | 8 |
| DOIs | |
| State | Published - 2006 |
Bibliographical note
Funding Information:This work is supported by the National Science Council of Taiwan under the grant contract NSC-94-2213-E-027-041.
Keywords
- Feature evaluation and selection
- Feature extraction
- Feature representation
- Geometric models
- Hough transform
- Object recognition
- Pattern analysis
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