Efficient Search and Verification for Function Based Classification from Real Range Images

Abstract

In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about 150 real raw range images of object instances from 10 classes.

Object concept modeling based on the relationship among appearance, usage and functions

“…In the past, there have been some works on the object recognition considering object functions [8]- [12]. In [8], physical properties are employed to recognize objects.…”