基于特征点和最小面积的曲线描述和匹配  

Curve representation and matching based on feature points and minimal area

作　　者：张桂梅[1] 任伟[1] 徐芬[1] 

机构地区：[1]南昌航空大学航空与机械工程学院,南昌330063

出　　处：《计算机应用》2009年第4期1159-1161,1164,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60742005);江西省自然科学基金资助项目(2007GZS2556);江西省教育厅科技项目(GJJ08219);南昌航空大学无损检测技术教育部重点实验室开放基金资助项目(ZD200629007)

摘　　要：为了对关键特征点相同而子曲线曲率不同的曲线进行识别,提出一种新的平面曲线的描述和匹配方法。基于关键特征点进行粗匹配,根据精度要求设定最小面积阈值在子曲线上重新采样点,定义了一种新的采样点的识别向量,并根据子曲线上采样点的识别向量构造了新的识别向量矩阵,最后根据识别向量矩阵的差异度度量子曲线的相似性。通过对所有子曲线的识别实现对整条曲线的识别。该识别方法逐层筛选、由粗到精,避免了冗余操作。实验表明该方法高效、可行。In order to recognize the curve whose feature points are the same but the curvature between the feature points is different, a new method for representing and recognizing the contour curve was proposed. First, feature points of the contour were extracted for the rough matching; then the sampling points of the sub-curve were obtained based on the precision requirement using the given minimal area threshold. A new recognition vector of sample points was defined, and a novel recognition vector matrix was constructed based on the recognition vector of sample points; last the similarity of the corresponding sub-curves was calculated by comparing the recognition vector matrix. The curve was recognized by recognizing their each sub-curve. The matching method was a process from simple to complex, thus many redundancies calculations were avoided. The experimental results show the proposed algorithm is efficient and feasible.

关 键 词：特征点 识别向量 识别向量矩阵 曲线描述 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]