Shape discrimination using invariants defined from higher-order spectra

Chandran, Vinod & Elgar, Steve (1991) Shape discrimination using invariants defined from higher-order spectra. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP-91), 14-17 April 1991, Toronto, Canada.

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An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

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8 citations in Scopus
4 citations in Web of Science®
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ID Code: 45573
Item Type: Conference Paper
Refereed: No
Keywords: pattern recognition, random noise, spectral analysis, Gaussian noise, amplification-invariant, bifrequency space, bispectral invariants, bolts, feature vector, higher-order spectra, integrated bispectrum, invariant parameters, one-dimensional shapes, parallel implementation, pattern classification, scale invariant phase, shape discrimination, translation invariant
DOI: 10.1109/ICASSP.1991.150112
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > OTHER INFORMATION AND COMPUTING SCIENCES (089900)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 1991 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 08 Sep 2011 22:45
Last Modified: 17 Jul 2017 00:10

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