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<p>The authors describe a hybrid approach to the problem of image segmentation in range data analysis, where hybrid refers to a combination of both region- and edge-based considerations. The range image of 3-D objects is divided into surface primitives which are homogeneous in their intrinsic differential geometric properties and do not contain discontinuities in either depth of surface orientation. The method is based on the computation of partial derivatives, obtained by a selective local biquadratic surface fit. Then, by computing the Gaussian and mean curvatures, an initial region-gased segmentation is obtained in the form of a curvature sign map. Two additional initial edge-based segmentations are also computed from the partial derivatives and depth values, namely, jump and roof-edge maps. The three image maps are then combined to produce the final segmentation. Experimental results obtained for both synthetic and real range data of polyhedral and curved objects are given.</p>
region based method; 3D object; edge based method; computerised picture processing; pattern recognition; differential geometry; image segmentation; range data analysis; surface primitives; surface orientation; partial derivatives; local biquadratic surface fit; curvature sign map; depth values; computerised pattern recognition; computerised picture processing; curve fitting; geometry

N. Yokoya and M. Levine, "Range Image Segmentation Based on Differential Geometry: A Hybrid Approach," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 643-649, 1989.
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