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Segmentation through variable-order surface fitting
Abstract
The solution of the segmentation problem requires a mechanism for partitioning the image array into low-level entities based on a model of the underlying image structure. A piecewise-smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete, noiseless image reconstruction based on the extracted functions and regions. Surface curvature sign labeling provides an initial coarse image segmentation, which is refined by an iterative region-growing method based on variable-order surface fitting. Experimental results show the algorithm's performance on six range images and three intensity images.>
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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
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Towards 3D Point cloud based object maps for household environments
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Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation
Fei, L., Lu, G., Jia, W., Teng, S., & Zhang, D. (2019) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2018.2795609 · Cited by 228
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Metadata
- Authors
- Besl, P. J., & Jain, R. C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1988-03
- Volume
- 10
- Issue
- 2
- Pages
- 167-192
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2099088762
- Cited by
- 1,074
- References
- 2
- FWCI
- 36.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- Besl, P. J., & Jain, R. C. (1988). Segmentation through variable-order surface fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.3881
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