【摘要】 特征参数的抽取,是实现地形图中点状符号自动识别的关键。本文根据点状符号在扫描图中可能出现的旋转、缩放等情况,归纳和总结出了４种易于计算的不变性特征,并给出了这些特征的提取方法和部分实验结果。这４种特征参数是:复杂度——用以描述符号外围的复杂程度;扁率——用以反映符号的扁平程度;比重——符号的黑色部分在其整个覆盖区域中所占的比例;偏心率——用以反映符号重心与符号的几何中心的偏离程度。这４种特征参数具有旋转不变性和大小不变性,且便于提取、容易计算。更多还原

【Abstract】 Feature extraction is the key of automated recognition of map symbols. Four two-dimensional invariant features of map symbols are summarized. The methods of the extraction of invariant features and a part of the experiment results are put forward.The four invariant features are the degree of complication of the periphery of the symbols , the breadth length ratio of the symbols, the proportion between the black pixels andall the pixels of a symbol, and the distance between the barycenter and the geometry center of a symbol.These four invariant feature parameters are characterized by invariability of rotation and size. A part of invariant characters has been proved in the paper. They are easy to be picked-up and the calculation is very simple.From the results of the experiment, we can see that the four feature parameters com-mendably describe the characters of the point symbols on maps, and they are not influenced by changing the size of scanning maps or rotating symbols and not disturbed by any noise of the image.The correctness ratio of automated recognition of point symbols on a map could be greatly increased through extracting and analyzing these four feature parameters.更多还原