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Research article summary:
Classification image weights and internal noise level estimation.
Abstract Extract:
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 ... (Full abstract text below) Published 2002
in Journal: J Vis
(Language : eng)
Full Pubmed Extract
This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:
1. J Vis.
2002 ;2(1):121-31
Classification image weights and internal noise level estimation.
Ahumada AJ
NASA Ames Research Center, Moffett Field, CA, USA. aahumada@mail.arc.nasa.gov
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.
PMID : 12678600 [PubMed - Indexed for MEDLINE]
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Full Author Information
| First Name | LastName | Initials |
| Albert J | Ahumada | AJ |
Affiliation: NASA Ames Research Center, Moffett Field, CA, USA. aahumada@mail.arc.nasa.gov
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