Classification image weights and internal noise level estimation | Zendy

Albert J. Ahumada | Zendy

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Classification image weights and internal noise level estimation

Author(s) -

Albert J. Ahumada

Publication year - 2002

Publication title -

journal of vision

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.126

H-Index - 113

ISSN - 1534-7362

DOI - 10.1167/2.1.8

Subject(s) - gaussian noise , pattern recognition (psychology) , mathematics , stimulus (psychology) , artificial intelligence , observer (physics) , white noise , noise (video) , image noise , statistics , additive white gaussian noise , computer science , psychology , image (mathematics) , physics , cognitive psychology , quantum mechanics

For the linear discrimination of two stimuli in white Gaussian noise in the presence of internal noise, a method is described for estimating linear classification weights from the sum of noise images segregated by stimulus and response. The recommended method for combining the two response images for the same stimulus is to difference the average images. Weights are derived for combining images over stimuli and observers. Methods for estimating the level of internal noise are described with emphasis on the case of repeated presentations of the same noise sample. Simple tests for particular hypotheses about the weights are shown based on observer agreement with a noiseless version of the hypothesis.

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