Hierarchical Wavelet-Based Image Model for Pattern Analysis and Synthesis

Files in this item

Files Size Format View
Sco2000Jul5Hierarchic.PDF 817.4Kb application/pdf Thumbnail

Show full item record

Item Metadata

Title: Hierarchical Wavelet-Based Image Model for Pattern Analysis and Synthesis
Author: Scott, Clayton; Nowak, Robert David
Type: Conference paper
xmlui.Rice_ECE.Keywords: Wavelets; pattern analysis; MDL
Citation: C. Scott and R. D. Nowak, "Hierarchical Wavelet-Based Image Model for Pattern Analysis and Synthesis," 2000.
Abstract: Despite their success in other areas of statistical signal processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations (e.g., translation, rotation, scaling) 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 illustrate template learning with examples, and discuss how TEMPLAR applies to pattern classification and denoising from multiple, unaligned observations.
Date Published: 2000-07-20

This item appears in the following Collection(s)

  • ECE Publications [1054 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.