The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
Abstract
Two different formal definitions of gray-scale reconstruction are presented. The use of gray-scale reconstruction in various image processing applications discussed to illustrate the usefulness of this transformation for image filtering and segmentation tasks. The standard parallel and sequential approaches to reconstruction are reviewed. It is shown that their common drawback is their inefficiency on conventional computers. To improve this situation, an algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels is introduced. Its combination with the sequential technique results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm.
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Cites
Image analysis and mathematical morphology
(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,151
Watersheds in digital spaces: an efficient algorithm based on immersion simulations
Vincent, L., & Soille, P. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.87344 · Cited by 5,649
Self-citationHaralock, R. M., & Shapiro, L. G. (1991) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 3,933
Image Analysis and Mathematical Morphology.
Diggle, P. J., & Serra, J. (1983) · Biometrics · doi:10.2307/2531038 · Cited by 3,894
Distance transformations in digital images
Borgefors, G. (1986) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(86)80047-0 · Cited by 1,898
Sequential Operations in Digital Picture Processing
Rosenfeld, A., & Pfaltz, J. L. (1966) · Journal of the ACM · doi:10.1145/321356.321357 · Cited by 1,675
Meyer, F., & Beucher, S. (1990) · Journal of Visual Communication and Image Representation · doi:10.1016/1047-3203(90)90014-m · Cited by 1,163
Use of watersheds in contour detection
Beucher, S. (1979) · Medical Entomology and Zoology · Cited by 1,100
Distance functions on digital pictures
Rosenfeld, A., & Pfaltz, J. L. (1968) · Pattern Recognition · doi:10.1016/0031-3203(68)90013-7 · Cited by 837
Mathematical Morphology in Image Processing
Dougherty, E. (2018) · CRC Press eBooks · doi:10.1201/9781482277234 · Cited by 831
Cited by
Morphological Attribute Profiles for the Analysis of Very High Resolution Images
Dalla Mura, M., Benediktsson, J. A., Waske, B., & Bruzzone, L. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2048116 · Cited by 722
Salembier, P., & Garrido, L. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.841934 · Cited by 587
Detecting unfocused raindrops: In-vehicle multipurpose cameras
Cord, A., & Gimonet, N. (2014) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2013.2287451 · Cited by 35
Park, C., & Won, S. (2009) · 2009 35th Annual Conference of IEEE Industrial Electronics · doi:10.1109/iecon.2009.5415248 · Cited by 10
Metadata
- Authors
- Vincent, L.
- Venue
- IEEE Transactions on Image Processing
- Published
- 1993-04
- Volume
- 2
- Issue
- 2
- Pages
- 176-201
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2157840858
- Cited by
- 1,923
- References
- 10
- FWCI
- 22.62
- Type
- journal article
- Language
- en
- Open access
- closed
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