Adaptive fuzzy color segmentation with neural network for road detections

Chieh Li Chen, Chung Li Tai

研究成果: Article同行評審

22 引文 斯高帕斯(Scopus)


In this paper a new color space, called the RGB color ratio space, is proposed and defined according to a reference color such that an image can be transformed from a conventional color space to the RGB color ratio space. Because a color in the RGB color ratio space is represented as three color ratios and intensity, the chrominance can be completely reserved (three color ratios) and the luminance can be de-correlated with the chrominance. Different from traditional distance measurement, a road color model is determined by an ellipse area in the RGB ratio space enclosed by the estimated boundaries. A proposed adaptive fuzzy logic in which fuzzy membership functions are defined according to estimated boundaries is introduced to implement clustering rules. Therefore, each pixel will have its own fuzzy membership function corresponding to its intensity. A basic neural network is trained and used to achieve parameters optimization. The low computation cost of the proposed segmentation method shows the feasibility for real time application. Experimental results for road detection demonstrate the robustness to intensity variation of the proposed approach.

頁(從 - 到)400-410
期刊Engineering Applications of Artificial Intelligence
出版狀態Published - 2010 4月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 人工智慧
  • 電氣與電子工程


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