Adaptive fuzzy color segmentation with neural network for road detections

Chieh Li Chen, Chung Li Tai

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)400-410
Number of pages11
JournalEngineering Applications of Artificial Intelligence
Volume23
Issue number3
DOIs
Publication statusPublished - 2010 Apr

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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