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吴玲达1,2, 张友根2, 邓维1, 宋汉辰2(1.装备学院重点实验室, 北京 101416;2.国防科技大学信息系统与管理学院, 长沙 410073)

摘 要
目的 在笔式态势标绘应用中,识别手绘点状军标图形面临着图形类别多、图形类别之间相似度高、绘制方向可变等挑战。针对这些困难提出一个面向手绘军标图形的旋转自由识别方案,以识别图形类别和方向角为目标。方法 首先通过旋转不变的粗分类缩小候选类别范围,然后估计待识别图形与模板图形间的方向夹角并将二者旋转对齐,最后用细化区分方法识别高相似度的图形类别。采用一种结合图形采样点空间分布和局部方向信息的方向Zernike矩特征描述图形样本,通过匹配方向Zernike矩可实现粗分类和旋转角估计。结果 实验结果表明本文方法的分类准确率和角度估计精度均明显优于基于传统Zernike矩的识别方法。结论 该方法可有效应用于对在线手绘军标图形进行旋转自由识别的场合。
Rotation free recognition of hand-drawn military marking symbols

Wu Lingda1,2, Zhang Yougen2, Deng Wei1, Song Hanchen2(1.Key Laboratory, Academy of Equipment, Beijing 101416, China;2.College of Information System and Management, National University of Defense Technology, Changsha 410073, China)

Objective In pen-based military situation marking systems, recognizing hand-drawn symbols is confronted with several challenges, such as numerous classes of graphic symbols, high similarity between classes, and orientation variation of many rotatable symbols.A rotation free recognition paradigm is presented considering these difficultiesand, aiming at classifying an instance of a symbol as well as estimating its rotation angle.Method First, rotation invariant coarse classification is performed to narrow the range of candidate classes.Then the rotation angle between the unknown instance and the template instance is estimated. They can be rotationally aligned by compensating the rotation angle between them.Finally, fining classification methods can be applied to distinguish similar symbols.A novel Zernike moments-based descriptor, called DZM, was used to represent hand-drawn symbol samples.It combines the spatial distribution of sample points and their local direction information.By matching DZM features, both coarse classification and rotation angle estimation could be accomplished.Result Experimental results show that the proposed method outperforms the traditional Zernike moment method in both classification and rotation angle estimation of hand-drawn military situation marking symbols.Conclusion This method can be applied effectively in rotation free recognition of online hand-drawn military marking symbols.