Multichannel filter for texture analysis: an adaptive selector approach

Udom Silparcha; George W. Gerrity; T. Graham Freeman

doi:10.1117/12.316408

6 July 1998 Multichannel filter for texture analysis: an adaptive selector approach

Udom Silparcha, George W. Gerrity, T. Graham Freeman

Proceedings Volume 3387, Visual Information Processing VII; (1998) https://doi.org/10.1117/12.316408
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

A concept to improve the accuracy in an unsupervised texture segmentation is presented in this paper. In a supervised segmentation, some known information or human intervention is provided to achieve a good segmentation result. However, unlike the supervised method, an unsupervised approach does not require any known information about the image to be segmented. This restriction can make the approach less accurate than the former method. To overcome such a restriction, we propose a mechanism to automatically obtain the information about the image before the actual segmentation process begins. This is possible by introducing a pre- segmentation step to obtain preliminary information about the image. Such self-generated information is called pseudo-a priori knowledge because it is not known or supplied to the segmentation process. The information is useful as a guideline in the selection of a critical set of Gabor filter parameters which in turn improve the accuracy in texture segmentation. The segmentation results of the proposed pseudo-supervised approach are compared with the results from a typical segmentation approach using a fixed quasi-complete Gabor filter set by applying them to the same test images. The segmentation results are compared using the percentage of misclustered pixels.

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Udom Silparcha, George W. Gerrity, and T. Graham Freeman "Multichannel filter for texture analysis: an adaptive selector approach", Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); https://doi.org/10.1117/12.316408

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KEYWORDS

Image segmentation

Image filtering

Digital filtering

Image processing

Optimal filtering

Image processing algorithms and systems

Data processing

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