Symbolic Signatures for Deformable Shapes

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 ...

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila, Gabriel Berson, Michael L. Cunningham, Raymond W. Sze
• Keywords: Complex Geometrical Information, Object Classes, PAMI 2006, Relevant Object Classes