24 October 2005 Trapezium model-based crop field structure recognition for guidance system of off-road vehicle
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Abstract
The trapezium models are designed for matching with the intensity outlines to locate the crop rows. Tow kinds of model were designed, single trapezium and double trapezium model. The former was applied to single grass row, while the later was applied to double maize rows. The intensity outlines were extracted by summing the intensities in each column. To locate the crop row quickly, a fast position algorithm was designed that a predigested trapezium model was constructed first according to the distribution of gray level, and then detail model located the row position accurately. The location of maximum correlation coefficients between the model and real intensity data were thought as the position of crop row. The mean correlation coefficient of single trapezium model at the location of row is 0.91, and that of double model is 0.7. This approach has been experimented on field of ZJU in real time and it is proved work robust.
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Yibin Ying, Fangming Zhang, Huanyu Jiang, Chuan Shen, "Trapezium model-based crop field structure recognition for guidance system of off-road vehicle", Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 60060P (24 October 2005); doi: 10.1117/12.630692; https://doi.org/10.1117/12.630692
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