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A fast learning algorithm for Gabor transformation

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dc.contributor.authorIbrahim, Ayman, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:43:35Z
dc.date.available2007-01-03T04:43:35Z
dc.date.issued1996
dc.description.abstractAn adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor transform representation is introduced in this correspondence. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationIbrahim, Ayman and Mahmood R. Azimi-Sadjadi, A Fast Learning Algorithm for Gabor Transformation, IEEE Transactions on Image Processing 5, no.1 (January 1996): 171-175.
dc.identifier.urihttp://hdl.handle.net/10217/848
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1996 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectimage representation
dc.subjectconvergence of numerical methods
dc.subjectlearning systems
dc.subjectadaptive systems
dc.subjecttransforms
dc.subjectdata compression
dc.subjectleast squares approximations
dc.subjectrecursive estimation
dc.subjectdata reduction
dc.subjectimage reconstruction
dc.titleA fast learning algorithm for Gabor transformation
dc.typeText

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