Stroke Fragmentation Using Geometry Features and Hidden Markov Model
Guihuan Feng, Zhengxing Sun, Viard-Gaudin Christian · Journal of Software · 2009
图元分解是笔交互的关键环节.提出一种将几何特征和隐马尔可夫模型HMM(hidden Markov model)结合的笔画图元分解方法.该方法采用4种关键几何特征来描述笔画的局部几何信息,并通过HMM结构对绘制上下文的建模来描述笔画的全局几何特性,进而利用全局搜索与最佳匹配实现在尽可能不丢失笔画几何信息的前提下对数据进行最大限度的压缩,可在保证系统计算效率的前提下同时完成分割点的查找与图元类型的判定.实验结果表明了该方法的有效性.;Stroke fragmentation is the core of the pen-based interaction. This paper presents a novel method of stroke fragmentation, which combines geometric features and HMM (hidden Markov model). Four geometric features are employed to describe the local geometry of strokes, and a HMM structure is designed to model the drawing context to describe the global. Furthermore, stroke data is compressed as much as possible with the least loss of information by means of global searching and the best matching algorithm. It can locate the segment point and judge the primitive type simultaneously with acceptable computation efficiency. Experimental results show the effectiveness of the proposed method.