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The Adaptive Hough Transform
Abstract
We introduce the Adaptive Hough Transform, AHT, as an efficient way of implementing the Hough Transform, HT, method for the detection of 2-D shapes. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. The method is substantially superior to the standard HT implementation in both storage and computational requirements. In this correspondence we illustrate the ideas of the AHT by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces. We show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.
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Cited by
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Taleb, A., Kirchler, M., Monti, R., & Lippert, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02024 · Cited by 83
A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars
Jiang, F., Wang, Z., & Yue, G. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10215-7 · Cited by 9
Metadata
- Authors
- Illingworth, J., & Kittler, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-09
- Volume
- PAMI-9
- Issue
- 5
- Pages
- 690-698
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2004491626
- Cited by
- 693
- References
- 0
- FWCI
- 18.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The Adaptive Hough Transform. https://app.cochat.ai/papers/10.1109/tpami.1987.4767964
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- Illingworth, J., & Kittler, J. (1987). The Adaptive Hough Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.1987.4767964
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