DocumentCode
384193
Title
Representing and recognizing complete set of geons using extended superquadrics
Author
Zhou, Lin ; Kambhamettu, Chandra
Author_Institution
Dept. of Comput. & Inf. Sci., Delaware Univ., Newark, DE, USA
Volume
3
fYear
2002
fDate
2002
Firstpage
713
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.
Keywords
backpropagation; image classification; image recognition; image representation; neural nets; object recognition; backpropagation neural network; classification error rates; extended superquadrics; geon recognition; geon representation; latitude angles; longitude angles; nearest neighbor classifier; object recognition system; spherical coordinate system; volumetric shape models; Buildings; Fires; Image databases; Object recognition; Power system modeling; Psychology; Reflection; Shape; Solid modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-1695-X
Type
conf
DOI
10.1109/ICPR.2002.1048038
Filename
1048038
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