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26 August 1999 Quality classification of wooden surfaces using Gabor filters and genetic feature optimization
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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.
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Wolfgang Poelzleitner and Gert Schwingskakl "Quality classification of wooden surfaces using Gabor filters and genetic feature optimization", Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); https://doi.org/10.1117/12.360301
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