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Issue No.02 - March (1988 vol.10)
pp: 167-192
<p>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.</p>
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
P.J. Besl, R.C. Jain, "Segmentation through Variable-Order Surface Fitting", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.10, no. 2, pp. 167-192, March 1988, doi:10.1109/34.3881
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