Function-Based Classification From 3D Data Via Generic And Symbolic Models
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Function-Based Classification from 3D Data via Generic and Symbolic Models.
In AAAI, 950-955, 2005
Online Version
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Abstract
We propose a novel scheme for function-based classification of objects in 3D images. The classification process calls for constructing a generic multi-level hierarchical description of object classes in terms of functional components. Functionality is derived from a large set of geometric attributes and relationships between object parts. Initially, the input range data describing each object instance is segmented, each object part is labeled as one of a few possible primitives, and each group of primitive parts is tagged by a functional symbol. Connections between primitive parts and functional parts at the same level in the hierarchy are labeled as well. Then, the generic multi-level hierarchical description of object classes is built using the functionalities of a number of object instances. During classification, a search through a finite graph using a probabilistic fitness measure is performed to find the best assignment of object parts to the functional structures of each class. An object is assigned to a class providing the highest fitness value. The scheme does not require a-priori knowledge about any class. We tested the proposed scheme on a database of about one thousand different 3D objects. The results show high accuracy in classification.
Keywords
Co-authors
Bibtex Entry
@inproceedings{PechukSR05i,
title = {Function-Based Classification from 3D Data via Generic and Symbolic Models.},
author = {Michael Pechuk and Octavian Soldea and Ehud Rivlin},
year = {2005},
booktitle = {AAAI},
pages = {950-955},
keywords = {Function},
abstract = {We propose a novel scheme for function-based classification of objects in 3D images. The classification process calls for constructing a generic multi-level hierarchical description of object classes in terms of functional components. Functionality is derived from a large set of geometric attributes and relationships between object parts. Initially, the input range data describing each object instance is segmented, each object part is labeled as one of a few possible primitives, and each group of primitive parts is tagged by a functional symbol. Connections between primitive parts and functional parts at the same level in the hierarchy are labeled as well. Then, the generic multi-level hierarchical description of object classes is built using the functionalities of a number of object instances. During classification, a search through a finite graph using a probabilistic fitness measure is performed to find the best assignment of object parts to the functional structures of each class. An object is assigned to a class providing the highest fitness value. The scheme does not require a-priori knowledge about any class. We tested the proposed scheme on a database of about one thousand different 3D objects. The results show high accuracy in classification.}
}