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Symbolic Signatures for Deformable Shapes Salvador Ruiz-Correa, Linda G. Shapiro, Fellow, IEEE, Marina Meila,

Summary: Symbolic Signatures for Deformable Shapes
Salvador Ruiz-Correa, Linda G. Shapiro, Fellow, IEEE, Marina Meila,
Gabriel Berson, Michael L. Cunningham, and Raymond W. Sze
Abstract--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.

OBJECT recognition from shape has always been an
important topic in computer vision research, but only


Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle


Collections: Computer Technologies and Information Sciences