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http://dx.doi.org/10.5302/J.ICROS.2002.8.6.468

Determination of Road Image Quality Using Fuzzy-Neural Network  

이운근 (부산대학교 전자공학과)
백광렬 (부산대학교 전자전기정보컴퓨터공학부)
이준웅 (전남대학교 산업공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.8, no.6, 2002 , pp. 468-476 More about this Journal
Abstract
The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.
Keywords
lane-related information; cumulative distribution function (CDF); fuzzy-neural network; image processing; image quality;
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