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Inherent bias and noise in the hough transform
1Department of Computer Science, University of Rochester, Rochester, NY 14627.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study reframes the Hough transformation as a linear imaging process, improving understanding of its behavior in noisy conditions. Novel methods using complementary votes sharpen parameter space peaks and reduce bias.
Area of Science:
- Computer Vision
- Image Processing
- Signal Processing
Background:
- The Hough transformation is a key technique for detecting shapes in images.
- Its performance can degrade significantly in the presence of noise.
- Existing literature may not fully address noise mitigation strategies.
Purpose of the Study:
- To reframe the Hough transformation as a linear imaging process.
- To develop methods for enhancing Hough transform peak detection accuracy.
- To reduce bias and improve robustness in noisy image data.
Main Methods:
- Conceptualizing the Hough transformation as a linear imaging process.
- Analyzing the analytic form of peaks in parameter space.
- Implementing a novel approach using complementary (negative) votes to cancel off-peak votes.
Main Results:
- Sharpened peaks in parameter space.
- Reduced bias in detected shapes.
- Improved understanding of Hough transformation behavior under noise.
Conclusions:
- Viewing the Hough transformation as a linear imaging process offers a new perspective.
- Complementary voting is an effective technique for improving Hough transform accuracy and robustness.
- This approach provides a useful vocabulary and points to relevant literature for noise analysis.
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