Quantitative fuzzy measures threshold selection

doi:10.1016/S0167-8655(99)00120-8

Quantitative fuzzy measures threshold selection

2000-01-01

30

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AI

DOI：10.1016/S0167-8655(99)00120-8

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Discrimination of Image Qualities for some applications in Computer Vision is very much important. The process of evaluation of image quality is very difficult as it possesses noise which is fuzzy in nature. Threshold selection for object extraction, for. bimodal or multi modal histogram is still a difficult problem. It is proposed in this paper that based on the four fuzzy measures, namely, linear and quadratic index of fuzziness, logarithmic and exponential entropy measures, how the best threshold will be identified and used. A comparative study of four such fuzzy measures for real life images has been carried out and promising results are obtained. Selection of the best threshold is tried out using a neural network (BPN) as it helps for fine tuning. (C) 2000 Elsevier Science B.V. All rights reserved.

Keyword:

image enhancement

fuzzy measures

threshold, object extraction

fuzzy neuro approach, comparison of performance

Quantitative fuzzy measures threshold selection

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