NDLI: Representing and recognizing complete set of geons using extended superquadrics

Content Provider	IEEE Xplore Digital Library
Author	Zhou, L. Kambhamettu, C.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Delaware Univ., Newark, DE, USA (Zhou, L.; Kambhamettu, C.)
Abstract	In this paper, we take advantage of extended superquadrics to represent and recognize the entire set of 36 geons. Extended superquadrics are novel volumetric shape models that include superquadrics as a special case. An extended superquadric model can be deformed in any direction because it extends the exponents of the superquadric model from constants to functions of the latitude and longitude angles in the spherical coordinate system. Thirteen features derived from the extended superquadric parameters are recovered in order to distinguish between all 36 geon classes. Classification error rates are estimated for the nearest neighbor classifier and backpropagation neural network. Both simulated data (at different noise levels) and real geon models are tested in our experiments. The results are very encouraging and have significant benefits for an object recognition system.
Starting Page	713
Ending Page	718
File Size	387035
Page Count	6
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048038
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Object recognition Reflection Fires Psychology Power system modeling Solid modeling Image databases Buildings
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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