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Proceedings Paper

Regularization based on steering parameterized Gaussian filters and a Bhattacharyya distance functional
Author(s): Emerson Prado Lopes
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Paper Abstract

Template regularization embeds the problem of class separability. In the machine vision perspective, this problem is critical when a textural classification procedure is applied to non-stationary pattern mosaic images. These applications often present low accuracy performance due to disturbance of the classifiers produced by exogenous or endogenous signal regularity perturbations. Natural scene imaging, where the images present certain degree of homogeneity in terms of texture element size or shape (primitives) shows a variety of behaviors, especially varying the preferential spatial directionality. The space-time image pattern characterization is only solved if classification procedures are designed considering the most robust tools within a parallel and hardware perspective. The results to be compared in this paper are obtained using a framework based on multi-resolution, frame and hypothesis approach. Two strategies for the bank of Gabor filters applications are considered: adaptive strategy using the KL transform and fix configuration strategy. The regularization under discussion is accomplished in the pyramid building system instance. The filterings are steering Gaussians controlled by free parameters which are adjusted in accordance with a feedback process driven by hints obtained from sequence of frames interaction functionals pos-processed in the training process and including classification of training set samples as examples. Besides these adjustments there is continuous input data sensitive adaptiveness. The experimental result assessments are focused on two basic issues: Bhattacharyya distance as pattern characterization feature and the combination of KL transform as feature selection and adaptive criterion with the regularization of the pattern Bhattacharyya distance functional (BDF) behavior, using the BDF state separability and symmetry as the main indicators of an optimum framework parameter configuration.

Paper Details

Date Published: 21 August 2001
PDF: 9 pages
Proc. SPIE 4326, Smart Structures and Materials 2001: Modeling, Signal Processing, and Control in Smart Structures, (21 August 2001); doi: 10.1117/12.436513
Show Author Affiliations
Emerson Prado Lopes, Univ. of Surrey and Federal Univ. of Rio de Janeiro (Brazil)

Published in SPIE Proceedings Vol. 4326:
Smart Structures and Materials 2001: Modeling, Signal Processing, and Control in Smart Structures
Vittal S. Rao, Editor(s)

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