The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Authenticating edges produced by zero-crossing algorithms
Abstract
It is shown that zero-crossing edge detection algorithms can produce edges that do not correspond to significant image intensity changes. Such edges are called phantom or spurious. A method for classifying zero crossings as corresponding to authentic or phantom edges is presented. The contrast of an authentic edge is shown to increase and the contrast of phantom edges to decrease with a decrease in the filter scale. Thus, a phantom edge is truly a phantom in that the closer one examines it, the weaker it becomes. The results of applying the classification schemes described to synthetic and authentic signals in one and two dimensions are given. The significance of the phantom edges is examined with respect to their frequency and strength relative to the authentic edges, and it is seen that authentic edges are denser and stronger, on the average, than phantom edges.>
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Cites
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
Cited by
An adaptive Gaussian filter for noise reduction and edge detection
Deng, G., & Cahill, L. W. (2005) · 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference · doi:10.1109/nssmic.1993.373563 · Cited by 429
Edge detection in multispectral images
Cumani, A. (1991) · CVGIP: Graphical Models and Image Processing · doi:10.1016/1049-9652(91)90018-f · Cited by 278
Metadata
- Authors
- Clark, J. J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1989
- Volume
- 11
- Issue
- 1
- Pages
- 43-57
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2074084241
- Cited by
- 203
- References
- 1
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Clark, J. J. (1989). Authenticating edges produced by zero-crossing algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.23112
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