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<p><b>Abstract</b>—We address the problem of recovering high-level, volumetric and segmented (or part-based) descriptions of objects from intensity images. As input we use three closely spaced images of an object and recover descriptions based on Generalized Cylinders (GCCs). We start by extracting a hierarchy of groups from contour images in the three views. Grouping is based on proximity, parallelism, and symmetry. The groups in the three views are matched and their contours are labeled as "true" edges, which correspond to surface normal/reflectance discontinuities and "limb" edges, which are viewpoint dependent edges. We then infer the GC axis, its cross-section, and the scaling function. The properties of straight and curved axis generalized cylinders are used locally on the visible surfaces to obtain the GC axis. The cross-section is recovered if seen in the images, else it is inferred using the visible surfaces and GC properties. We consider groups with true edges, limb edges, or a combination of both. The final descriptions are volumetric and in terms of parts. The coarse volumetric descriptions obtained are then refined to include surface details as seen in the intensity images. We demonstrate results on real images of moderately complex objects with texture and shadows.</p>
Shape description, grouping, stereo, generalized cylinders.

P. Havaldar and G. Medioni, "Full Volumetric Descriptions From Three Intensity Images," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 540-545, 1998.
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