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Green Image
Issue No. 02 - March (1988 vol. 10)
ISSN: 0162-8828
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

R. Jain and P. Besl, "Segmentation through Variable-Order Surface Fitting," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 10, no. , pp. 167-192, 1988.
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