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Signal processing for feature extraction and pattern recognition
Conference proceeding

Signal processing for feature extraction and pattern recognition

B Eisenstein and J Fehlauer
ICASSP '76. IEEE International Conference on Acoustics, Speech, and Signal Processing, v 1, pp 749-752
1976

Abstract

Covariance matrix Eigenvalues and eigenfunctions Extraterrestrial measurements Feature extraction Particle measurements Pattern recognition Probability density function Scattering Signal processing Vectors
A new feature extraction technique designed specifically for the mass screening problem is presented. It is based on the hypothesis that feature values from the normal class tend to form a cluster while those from the abnormal class fall outside this cluster, or "decluster". While some feature extraction techniques attempt to retain the fidelity of the data from which the features arose, the new technique is designed to enhance the discrimination between the normal and abnormal classes. Results from applying the new algorithm to the mass screening of breast thermograms to detect breast cancer are discussed.

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