Abstract

Humans are adept at size classification from visual images of objects. A challenging computer vision problem is that of automatic visual size classification. Current size classification systems assume controlled environments and use features geared towards a particular object category and pose. However, certain applications may require algorithms that can adapt to a variety of object categories and handle complex environments. In this paper, we propose a Bayesian approach to automatic visual size classification, inspired by human visual perception, for a more generalized and robust size classifier. Initial results show that the proposed approach can handle multiple object categories and is invariant to scale changes.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages255-258
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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    McDaniel, T., Kahol, K., & Panchanathan, S. (2006). A bayesian approach to visual size classification of everyday objects. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 255-258). [1699195] (Proceedings - International Conference on Pattern Recognition; Vol. 2). https://doi.org/10.1109/ICPR.2006.37