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Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly difficult to implement, not only because the shape variability of relevant object classes is generally large, but also because standard sensing devices used to capture the real world only provide a partial view of a scene, so there is partial information pertaining to the objects of interest. In this work, we develop an algorithmic framework for recognizing classes of deformable shapes from range data. The basic idea of our component-based approach is to generalize existing surface representations that have proven effective in recognizing specific 3D objects to the problem of object classes using our newly introduced symbolic-signature representation that is robust to deformations, as opposed to a numeric representation that is often tied to a specific shape. Based on this approach, we present a system that is capable of recognizing and classifying a variety of object shape classes from range data. We demonstrate our system in a series of large-scale experiments that were motivated by specific applications in scene analysis and medical diagnosis.
Index Terms- Three-dimensional object recognition and classification, deformable shapes, range data, numeric and symbolic signatures, Mercer kernel, scene analysis, craniosynostosis, craniofacial malformations.
Salvador Ruiz-Correa, Raymond W. Sze, Marina Meila, Michael L. Cunningham, Gabriel Berson, Linda G. Shapiro, "Symbolic Signatures for Deformable Shapes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 75-90, January 2006, doi:10.1109/TPAMI.2006.23
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