Discrete Fourier transform based pattern classifiers

• Autorzy: Hui S. , Żak S. H.

Identyfikatory

DOI

10.2478/bpasts-2014-0002

• Języki publikacji: EN

Abstrakty

EN

A technique for pattern classification using the Fourier transform combined with the nearest neighbor classifier is proposed. The multidimensional fast Fourier transform (FFT) is applied to the patterns in the data base. Then the magnitudes of the Fourier coefficients are sorted in descending order and the first P coefficients with largest magnitudes are selected, where P is a design parameter. These coefficients are then used in further processing rather than the original patterns. When a noisy pattern is presented for classification, the pattern’s P Fourier coefficients with largest magnitude are extracted. The coefficients are arranged in a vector in the descending order of their magnitudes. The obtained vector is referred to as the signature vector of the corresponding pattern. Then the distance between the signature vector of the pattern to be classified and the signature vectors of the patterns in the data base are computed and the pattern to be classified is matched with a pattern in the data base whose signature vector is closest to the signature vector of the pattern being classified.

Słowa kluczowe

EN

pattern classification; multidimensional discrete Fourier transform; DFT; Fourier coefficients

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2014
• Tom: Vol. 62, nr 1
• Strony: 15--22
• Opis fizyczny: Bibliogr. 19 poz., rys., wykr.

Twórcy

autor

Hui S.

• Department of Mathematical Sciences, San Diego State University, San Diego, CA 92182, USA

autor

Żak S. H.

• School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA

Bibliografia

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