摘要
地图是由表示各种地物及其空间关系的大量曲线和符号组成,本文在Freeman码的基础上,针对地图的具体特点,提出了一种适于地图要素轮廓描述的“预测跟踪”技术,在识别过程中,充分利用了地图的先验知识,通过学习来抽取地图各要素的分析特征.最后,本文给出以二值化、大比例尺的地图为实验对象的实验结果.
A typical geographical map consists of a large number of lines, curves and symbols representing various physical entities and their spatial relationships. Based on the principle of Freeman's China code, the authors propose a 'predicting-while-tracing' technology, which is suitable for the contour recognition and representation of the elements of a map. In the process of recognition, the prior knowledge of maps is used sufficiently. The analytic features of the elements of a map are extracted through recognition with learning. Finally, experiment results on a number of binary valued large scale maps are also presented.
出处
《自动化学报》
EI
CSCD
北大核心
1991年第1期77-82,共6页
Acta Automatica Sinica
关键词
预测跟踪
地图识别
Map
freeman's chain code
predictable-while-tracing
