Colour-based model pruning for efficient ARG object recognition

In this paper we address the problem of object recognition from 2D views. A new approach is proposed which combines the recognition systems based on attribute relational graph matching (ARG) and the multimodal neighbourhood signature (MNS) method. In the new system we use the MNS method as a pre-mat...

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Bibliographic Details
Published inObject recognition supported by user interaction for service robots Vol. 3; pp. 20 - 23 vol.3
Main Authors Ahmadyfard, A., Kittler, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Computer vision
Entropy
Image recognition
Labeling
Layout
Object recognition
Signal processing
Solid modeling
Speech processing
Testing
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Summary:In this paper we address the problem of object recognition from 2D views. A new approach is proposed which combines the recognition systems based on attribute relational graph matching (ARG) and the multimodal neighbourhood signature (MNS) method. In the new system we use the MNS method as a pre-matching stage to prune the number of model candidates. The ARG method then identifies the best model among the candidates through a relaxation labelling process. The results of experiments show a considerable gain in the ARG matching speed. Interestingly, as a result of the reduction in the entropy of labelling by a virtue model pruning, the recognition rate for extreme object views also improves.
ISBN:076951695X
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2002.1047785