한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)
- 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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- Pages.255-262
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- 1998
Robust 2-D Object Recognition Using Bispectrum and LVQ Neural Classifier
- HanSoowhan (Dept. of Multimedia Eng., Dongeui University) ;
- woon, Woo-Young (Dept. of Computer Eng., Dongeui University)
- 발행 : 1998.10.01
초록
This paper presents a translation, rotation and scale invariant methodology for the recognition of closed planar shape images using the bispectrum of a contour sequence and the learning vector quantization(LVQ) neural classifier. The contour sequences obtained from the closed planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The higher order spectra based on third order cumulants is applied to tihs contour sample to extract fifteen bispectral feature vectors for each planar image. There feature vector, which are invariant to shape translation, rotation and scale transformation, can be used to represent two0dimensional planar images and are fed into a neural network classifier. The LVQ architecture is chosen as a neural classifier because the network is easy and fast to train, the structure is relatively simple. The experimental recognition processes with eight different hapes of aircraft images are presented to illustrate the high performance of this proposed method even the target images are significantly corrupted by noise.
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