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
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;
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
Print_ISBN :
0-7695-1695-X
DOI :
10.1109/ICPR.2002.1048038