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Proceedings Paper

Qualitative optimization of image processing systems using random set modeling
Author(s): Patrick A. Kelly; Haluk Derin; Priya G. Vaidya
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Paper Abstract

Many decision-making systems involve image processing that converts input sensor data into output images having desirable features. Typically, the system user selects some processing parameters. The processor together with the input image can then be viewed as a system that maps the processing parameters into output features. However, the most significant output features often are not numerical quantities, but instead are subjective measures of image quality. It can be a difficult task for a user to find the processing parameters that give the 'best' output. We wish to automate this qualitative optimization task. The key to this is incorporation linguistic operating rules and qualitative output parameters in a numerical optimization scheme. In this paper, we use the test system of input parameter selection for 2D Wiener filtering to restore noisy and blurred images. Operating rules represented with random sets are used to generate a nominal input-output system model, which is then used to select initial Wiener filter input parameters. Whenthe nominally optimal Wiener filter is applied to an observed image, the operator's assessment of output image quality is used in an adaptive filtering algorithm to adjust the model and select new input parameters. Test on several images have confirmed that with a few such iterations, a significant improvement in output quality is achieved.

Paper Details

Date Published: 4 August 2000
PDF: 10 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395065
Show Author Affiliations
Patrick A. Kelly, Univ. of Massachusetts/Amherst (United States)
Haluk Derin, Univ. of Massachusetts/Amherst (United States)
Priya G. Vaidya, Univ. of Massachusetts/Amherst (United States)


Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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