DocumentCode :
3590017
Title :
3D Mixed Invariant and its Application on Object Classification
Author :
Feng, Songhe ; Aouada, Djamila ; Krim, H. ; Kogan, I.
Author_Institution :
Dept. of ECE, NCSU, Raleigh, NC, USA
Volume :
1
fYear :
2007
Abstract :
A new integro-differential invariant for curves in 3D transformed by affine group action is presented in this paper. The derivatives involved are of the first order, and therefore this invariant is significantly less sensitive to noise than classical affine differential invariants, the simplest of which involves derivatives of order 5. A classification procedure based on characteristic curves of an object surface is considered using our proposed mixed invariants. Substantiating examples are provided to verify efficiency and discriminant power of the characteristic spatial curve based 3D object classification.
Keywords :
image classification; integro-differential equations; object detection; 3D mixed invariant; affine group action; discriminant power; integro-differential invariant; object classification; spatial curve; Application software; Computational complexity; Computer vision; Face recognition; Feature extraction; Noise robustness; Object recognition; Pattern recognition; Shape; Turning; 3D affine transformation; affine invariant; invariant feature; object classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
1-4244-0727-3
Type :
conf
DOI :
10.1109/ICASSP.2007.366716
Filename :
4217116
Link To Document :
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