Open AccessOptimal discrimination and classification of THz spectra in the wavelet domain
Author(s) -
Roberto Kawakami Harrop Galvão,
Sillas Hadjiloucas,
James Bowen,
Clarimar José Coelho
Publication year - 2003
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.11.001462
Subject(s) - wavelet , pattern recognition (psychology) , wavelet packet decomposition , optics , wavelet transform , artificial intelligence , stationary wavelet transform , computer science , fourier transform , discrete wavelet transform , classifier (uml) , filter bank , imaging spectrometer , filter (signal processing) , physics , mathematics , spectrometer , computer vision , mathematical analysis
In rapid scan Fourier transform spectrometry, we show that the noise in the wavelet coefficients resulting from the filter bank decomposition of the complex insertion loss function is linearly related to the noise power in the sample interferogram by a noise amplification factor. By maximizing an objective function composed of the power of the wavelet coefficients divided by the noise amplification factor, optimal feature extraction in the wavelet domain is performed. The performance of a classifier based on the output of a filter bank is shown to be considerably better than that of an Euclidean distance classifier in the original spectral domain. An optimization procedure results in a further improvement of the wavelet classifier. The procedure is suitable for enhancing the contrast or classifying spectra acquired by either continuous wave or THz transient spectrometers as well as for increasing the dynamic range of THz imaging systems.