Show simple item record

dc.contributor.authorScott, Clayton
Nowak, Robert David
dc.creatorScott, Clayton
Nowak, Robert David
dc.date.accessioned 2007-10-31T01:04:41Z
dc.date.available 2007-10-31T01:04:41Z
dc.date.issued 2001-04-20
dc.date.submitted 2001-04-20
dc.identifier.urihttps://hdl.handle.net/1911/20341
dc.description Conference Paper
dc.description.abstract Despite 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.
dc.language.iso eng
dc.subjectwavelets
pattern analysis
MDL
supervised learning
dc.subject.otherImage Processing and Pattern analysis
dc.title TEMPLAR: A Wavelet-Based Framework for Pattern Learning and Analysis
dc.type Conference paper
dc.date.note 2004-01-09
dc.citation.bibtexName inproceedings
dc.date.modified 2004-01-09
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordwavelets
pattern analysis
MDL
supervised learning
dc.citation.conferenceName IEEE Transactions on Signal Processing
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.citation C. Scott and R. D. Nowak, "TEMPLAR: A Wavelet-Based Framework for Pattern Learning and Analysis," 2001.


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1305]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

Show simple item record