Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement
Abstract
The general principles of detection, classification, and measurement of discontinuities are studied. The following issues are discussed: detecting the location of discontinuities; classifying discontinuities by their degrees; measuring the size of discontinuities; and coping with the random noise and designing optimal discontinuity detectors. An algorithm is proposed for discontinuity detection from an input signal S. For degree k discontinuity detection and measurement, a detector (P, Phi ) is used, where P is the pattern and Phi is the corresponding filter. If there is a degree k discontinuity at location t/sub 0/, then in the filter response there is a scaled pattern alpha P at t/sub 0/, where alpha is the size of the discontinuity. This reduces the problem to searching for the scaled pattern in the filter response. A statistical method is proposed for the approximate pattern matching. To cope with the random noise, a study is made of optimal detectors, which minimize the effects of noise. >
Cite
Text
Lee. "Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement." IEEE/CVF International Conference on Computer Vision, 1988. doi:10.1109/CCV.1988.590035Markdown
[Lee. "Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement." IEEE/CVF International Conference on Computer Vision, 1988.](https://mlanthology.org/iccv/1988/lee1988iccv-coping/) doi:10.1109/CCV.1988.590035BibTeX
@inproceedings{lee1988iccv-coping,
title = {{Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement}},
author = {Lee, David},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1988},
pages = {546-557},
doi = {10.1109/CCV.1988.590035},
url = {https://mlanthology.org/iccv/1988/lee1988iccv-coping/}
}