Title :
Estimation of Error Rates in Classification of Distorted Imagery
Author_Institution :
U.S. Naval Research Laboratory, Washington, DC 20375; National Institutes of Health, Bethesda, MD 20209.
fDate :
7/1/1984 12:00:00 AM
Abstract :
This correspondence considers the problem of matching image data to a large library of objects when the image is distorted. Two types of distortions are considered: blur-type, in which a transfer function is applied to Fourier components of the image, and scale-type, in which each Fourier component is mapped into another. The objects of the library are assumed to be normally distributed in an appropriate feature space. Approximate expressions are developed for classification error rates as a function of noise. The error rates they predict are compared with those from classification of artificial data, generated by a Gaussian random number generator, and with error rates from classification of actual data. It is demonstrated that, for classification purposes, distortions can be characterized by a small number of parameters.
Keywords :
Error analysis; Estimation error; Image analysis; Mean square error methods; Pattern classification; Probability density function; Signal processing; Speech processing; Speech recognition; Statistics; Image classification; feature extraction; image matching; pattern classification; pattern recognition;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1984.4767560