基于剪枝信任度传播的手写体目标形状匹配方法  

The Novel Method of Matching Handwriting's Shapes based on Pruned Belief Propagation

作　　者：谢莹[1,2] 许荣斌[2] 朱二周[2] 吴建国[2] 束永安[2] 程凡[2] 

机构地区：[1]安徽大学计算机教学部,安徽合肥230601 [2]安徽大学计算机科学与技术学院,安徽合肥230601

出　　处：《数学的实践与认识》2014年第9期158-163,共6页Mathematics in Practice and Theory

基　　金：安徽大学2012年校级质量工程项目(JYXM201247;JYXM201291);基于红外窗口和红外测温技术的变电站高压设备一体化温度监测方案及示范应用项目(Grant No.KF070018);智能视频分析核心算法研发(2014KJH010009)

摘　　要：手写体识别中,目标形状的匹配是较为重要的工作.为了提高手写体目标形状的匹配速度,提出一种新的匹配方法.由于手写体目标形状的几何先验知识已知,并可以采用少量的参数进行表示,新方法采用参数化可变形模板匹配目标形状,确定其后验概率模型,并定义剪枝信任度空间,依据信任度传播算法的特性,首次将剪枝信任度传播算法应用于求解可变形模板与目标形状之间的最佳匹配.实验结果显示,在灰度图像中,对手写体目标形状的轮廓检测与定位速度显著提高.提出将剪枝信任度传播方法应用于手写体目标形状的匹配工作,能够使得目标形状填补空白,应用于相关性较为稀疏的图模型中.In the study of handwriting＇s recognition, the matching of shapes is very im- portant. To improve the matching speed, a novel method is presented which can matches handwriting＇s shapes faster. Owing to the foregone of shapes＇ geometrical prior probability and a small quantity of parameters, the method matches shapes using parameterized de- formable template, and it can denote the posterior probability. Based on the characters of belief propagation, we defined a pruned belief state and it is for the first time that we apply the pruned belief propagation algorithm to find the best match between the template and shape. The experimental results show that the novel method boosts the matching speed in the grayscale image.

关 键 词：形状匹配 信任度传播 可变形模板 剪枝信任度 灰度图像 模式识别 匹配速度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]