Template Learning from Atomic Representations: A Wavelet-Based Approach to Pattern Analysis

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameNoneen_US
dc.contributor.authorScott, Claytonen_US
dc.contributor.authorNowak, Robert Daviden_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:04:44Zen_US
dc.date.available2007-10-31T01:04:44Zen_US
dc.date.issued2001-04-20en_US
dc.date.modified2002-09-23en_US
dc.date.note2002-09-23en_US
dc.date.submitted2001-04-20en_US
dc.descriptionConference Paperen_US
dc.description.abstractDespite the success of wavelet decompositions in other areas of statistical signal and image processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations (e.g., translation, rotation, location of lighting source) inherent in most pattern observations. In this paper we introduce a hierarchical wavelet-based framework for modeling patterns in digital images. This framework takes advantage of the efficient image representations afforded by wavelets, while accounting for unknown pattern transformations. Given a trained model, we can use this framework to synthesize pattern observations. If the model parameters are unknown, we can infer them from labeled training data using TEMPLAR (Template Learning from Atomic Representations), a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length (MDL) complexity regularization to learn a template with a sparse representation in the wavelet domain. We discuss several applications, including template learning, pattern classification, and image registration.en_US
dc.identifier.citationC. Scott and R. D. Nowak, "Template Learning from Atomic Representations: A Wavelet-Based Approach to Pattern Analysis," 2001.en_US
dc.identifier.urihttps://hdl.handle.net/1911/20342en_US
dc.language.isoengen_US
dc.subjectwaveleten_US
dc.subjectpattern analysisen_US
dc.subjectMDLen_US
dc.subjectsupervised learningen_US
dc.subject.keywordwaveleten_US
dc.subject.keywordpattern analysisen_US
dc.subject.keywordMDLen_US
dc.subject.keywordsupervised learningen_US
dc.subject.otherImage Processing and Pattern analysisen_US
dc.titleTemplate Learning from Atomic Representations: A Wavelet-Based Approach to Pattern Analysisen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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