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Proceedings Paper

Quality classification of wooden surfaces using Gabor filters and genetic feature optimization
Author(s): Wolfgang Poelzleitner; Gert Schwingskakl
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Paper Abstract

We apply a model of texture segmentation using multiple spatially and spectrally localized filters, known as Gabor filters, to the analysis of texture and effect regions found on wooden boards. Specifically we present a method to find an optimal set of parameters for a given 2D object detection method. The method uses banks of Gabor filters to limit the rang of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels a local classification of texture regions can be achieved. Unlike other methods applying Gabor filters, we do not use a full Gabor transform, but use feature selection techniques to maximize discrimination. The selection method uses a genetic algorithm to optimize various parameters of the system including Gabor weights, and the parameters of morphological pre-processing. We demonstrate the applicability of the method to the task of classifying wooden textures, and report experimental results using the proposed method.

Paper Details

Date Published: 26 August 1999
PDF: 12 pages
Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); doi: 10.1117/12.360301
Show Author Affiliations
Wolfgang Poelzleitner, Technical Univ. Graz and Sensotech Forschungs- und Entwicklungs GmbH (Austria)
Gert Schwingskakl, Sensotech Forschungs- und Entwicklungs GmbH (Austria)

Published in SPIE Proceedings Vol. 3837:
Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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