In order to get an ideal lane marks' edge in a road image, this paper USES a method which is based on maximum two-dimension entropy to pick-up lane mark edges.

为了得到比较理想的道路图像中车道标识的边缘，文中采用了基于二维直方图熵最大化的车道标识边缘提取方法。

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Firstly, we expatiate the segmentation algorithm theory of two dimension maximal entropy based on inheritance algorithm.

阐述了基于遗传算法的二维最大熵分割算法的原理及实现步骤。

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Converte the one dimension time series into two dimension spot figure, then study the distribution of the spot around the 360 degree directions, compute the quadrant information entropy.

将一维的表面肌电信号转换为二维的散点图，在这二维的平面空间中研究散点在一周360度的各个方向上的分布情况，提出了象限信息熵的概念。

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