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Segmentation through Variable-Order Surface Fitting
March 1988 (vol. 10 no. 2)
pp. 167-192

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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Index Terms:
surface curvature sign labelling; computerised picture processing; variable-order surface fitting; image structure; piecewise-smooth surface model; surface coherence; bivariate functions; noiseless image reconstruction; image segmentation; iterative region-growing method; computerised picture processing; iterative methods
Citation:
P.J. Besl, R.C. Jain, "Segmentation through Variable-Order Surface Fitting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 2, pp. 167-192, March 1988, doi:10.1109/34.3881
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