一个基于知识的道路图像理解系统

无人地面车辆（ Ａ Ｌ Ｖ）无论在军事上还是在民用上都有着很好的应用前景．作为 Ａ Ｌ Ｖ 核心技术之一的视觉系统，在相关领域一直受到关注．为了提高视觉系统的性能，文中提出了一个基于知识的道路景物理解系统．系统中采用了多种信息融合和多种模型匹配的道路识别方法．这种方法利用边沿信息内在的空间信息包容性和定位的精确性，采用多个选择性好的模板来避免不同方向边沿对边线提取和跟踪的干扰，同时利用区域信息的稳定性，结合景物知识，进行多层次的融合．实验结果表明，对大多数条件不好或几何结构复杂的道路图像都能给出满意的识别结果

A KNOWLEDGE-BASED ROAD SCENE UNDERSTANDING SYSTEM

ALV (autonomous land vehicle) has a alluring prospect both in military and civilian applications. As the one of the kernel techniques in ALV, vision system has received close attention in the past two decades. In the paper here, a road recognition algorithm combining fusing of multiple information and matching with multiple models, in a knowledge|based system KRUS is presented, with high performance achieved. In the algorithm, a unique edge detection method is developed, from which the benefit of good localization is received, while the disturbance of noise is reduced. Then the robust region information is fused with edge information, with the help of some knowledge of the scenes. The experiment results show that most of the ill|formed or complicate|structured images are very satisfying.