基于特征点和最小面积的曲线描述和匹配

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。

Curve representation and matching based on feature points and minimal area

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.