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

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Title: Template Learning from Atomic Representations: A Wavelet-Based Approach to Pattern Analysis
Author: Scott, Clayton; Nowak, Robert David
Type: Conference Paper
Keywords: wavelet; pattern analysis; MDL; supervised learning
Citation: C. Scott and R. D. Nowak,"Template Learning from Atomic Representations: A Wavelet-Based Approach to Pattern Analysis," in None,
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.
Date Published: 2001-04-20

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  • ECE Publications [1032 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.