Efficient Search And Verification for Function Based Classification From Real Range Images
Efficient Search and Verification for Function Based Classification from Real Range Images.
CVIU, 105:200-217, 2007
Online Version
A pdf version is available for download.
Abstract
In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in wich 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.
Keywords
- Function Based Reasoning,
- Recognition,
- Classification,
- Computer Vision,
- Raw Range Images,
- 3D Segmentation.
Co-authors
Bibtex Entry
@article{FroimovichRSS07a,
title = {Efficient Search and Verification for Function Based Classification from Real Range Images},
author = {Guy Froimovich and Ehud Rivlin and Ilan Shimshoni and Octavian Soldea},
year = {2007},
journal = {CVIU},
volume = {105},
pages = {200-217},
keywords = {Function based reasoning; Recognition; Classification; Computer vision; Raw range images; 3D segmentation},
abstract = {In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in wich 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.}
}