The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Image Analysis Using Mathematical Morphology
Abstract
For the purposes of object or defect identification required in industrial vision applications, the operations of mathematical morphology are more useful than the convolution operations employed in signal processing because the morphological operators relate directly to shape. The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations. Examples are given for each morphological concept and explanations are given for many of their interrelationships.
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Citations
Cites
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Cited by
Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014) · European Journal of Remote Sensing · doi:10.5721/eujrs20144723 · Cited by 538
Handbook of Computer Vision Algorithms in Image Algebra
Wilson, J. N., & Ritter, G. X. (2000) · CRC Press eBooks · doi:10.1201/9781420042382 · Cited by 434
Zhang, K., Yan, M., Huang, T., Zheng, J., & Li, Z. (2019) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2019.02.010 · Cited by 111
Nassar, A., Amer, K., ElHakim, R., & ElHelw, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00201 · Cited by 94
Automated ultrasound assessment of amniotic fluid index using deep learning
Cho, H. C., Sun, S., Min Hyun, C., Kwon, J.-Y., Kim, B., Park, Y., & Seo, J. K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101951 · Cited by 43
Metadata
- Authors
- Haralick, R. M., Sternberg, S. R., & Zhuang, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-07
- Volume
- PAMI-9
- Issue
- 4
- Pages
- 532-550
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2154741421
- Cited by
- 2,710
- References
- 10
- FWCI
- 47.80
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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