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Parameter estimation for two-dimensional vector models using neural networks

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dc.contributor.authorXu, Lin, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:49:19Z
dc.date.available2007-01-03T04:49:19Z
dc.date.issued1995
dc.description.abstractThis correspondence addresses the problem of two-dimensional (2-D) vector image model parameter estimation using a new recursive least squares (RLS)-based learning method. Vector autoregressive (AR) models with various 1-D and 2-D, causal and noncausal regions of support (ROS) are considered. Numerical results are presented which demonstrate the usefulness of the proposed scheme for on-line implementation.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationXu, Lin and Mahmood R. Azimi-Sadjadi, Parameter Estimation for Two-Dimensional Vector Models Using Neural Networks, IEEE Transactions on Signal Processing 43, no. 12 (December 1995): 3090-3094.
dc.identifier.urihttp://hdl.handle.net/10217/936
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1995 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.subjectparameter estimation
dc.subjectautoregressive processes
dc.subjectconvergence of numerical methods
dc.subjectimage processing
dc.subjectlearning (artificial intelligence)
dc.subjectleast squares approximations
dc.subjectneural nets
dc.subjectvectors
dc.titleParameter estimation for two-dimensional vector models using neural networks
dc.typeText

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