대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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- Pages.761-764
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- 2000
퍼지신경망을 이용한 도로 씬의 차선정보의 잡음도 판별
Fuzzy Neural Network-Based Noisiness Decision of Road Scene for Lane Detection
- Yi, Un-Kun (Pusan National University) ;
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Baek, Kwang-Ryul
(Pusan National University) ;
- Kwon, Seok-Geon (Ulsan University) ;
- Lee, Joon-Woong (Chonnam National University)
- 발행 : 2000.11.25
초록
This paper presents a Fuzzy Neural Network (FNN) system to decide whether or not the right information of lanes can be extracted from gray-level images of road scene. The decision of noisy level of input images has been required because much noises usually deteriorates the performance of feature detection based on image processing and lead to erroneous results. As input parameters to FNN, eight noisiness indexes are constructed from a cumulative distribution function (CDF) and proved the indexes being classifiers of images as the good and the bad corrupted by sources of noise by correlation analysis between input images and the indexes. Considering real-time processing and discrimination efficiency, the proposed FNN is structured by eight input parameters, three fuzzy variables and single output. We conduct much experiments and show that our system has comparable performance in terms of false-positive rates.
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