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。
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。
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。
Then, inputting the two-dimension spectrum entropy into the SVM classification, the result shows that SVM has fine compute efficiency and classified ability.
进一步将二维谱熵作为支持向量机（SVM）分类器的输入量，结果表明SVM具有良好的分类能力和计算效率，可以满足在线监测的要求。
Then, inputting the two-dimension spectrum entropy into the SVM classification, the result shows that SVM has fine compute efficiency and classified ability.
进一步将二维谱熵作为支持向量机（SVM）分类器的输入量，结果表明SVM具有良好的分类能力和计算效率，可以满足在线监测的要求。
应用推荐