目的 针对仿射变换下形状匹配中存在的描述子对形状的描述能力不足，以及描述子计算耗时大的问题，改进基于所有图像点投影的方法，提出一种利用轮廓计算投影面积的仿射形状匹配算法。方法 该算法分为粗匹配和精匹配两个阶段。粗匹配阶段以CSS角点作为备选特征点，首先统计轮廓投影面积分布作为特征点描述子；然后利用动态规划蚁群算法匹配两幅图片公共特征点序列，并将匹配好的特征点序列记为对应的新特征点；最后采用该新特征点划分目标曲线，得到对应的轮廓曲线；这一阶段的目的是对形状的筛选以及寻找一致的轮廓特征点，同时完成轮廓曲线的划分。精匹配阶段，采用小波仿射不变描述子，对粗匹配阶段匹配代价最小的5%的目标进行对应曲线匹配，得到精匹配阶段的匹配代价，从而实现对仿射目标的识别；精匹配弥补了描述子对轮廓细节描述不足的问题。结果 算法的平均检索速度比传统基于形状投影分布描述子提高44.3%，在MPEG-7图像库上的检索效果为98.65%，在MPEG-7仿射图像库上的查准率与查全率综合评价指标比传统的基于形状投影分布描述子高3.1%，比形状上下文高25%。结论 本文算法匹配效果好，效率高，抗噪性强，解决了仿射描述子计算速度慢、描述能力不足的问题，能有效地应用于仿射形状匹配与检索领域。

Objective An affine shape matching method using a projection area calculated with a contour is proposed to improve the computation speed and the discrimination ability of a descriptor during shape matching.Method The algorithm can be divided into the coarse and fine matching stages.The coarse matching stage aims to select the shape and find consistent feature points.Area is an important affine invariant.In the coarse matching stage,we use CSS corner points as alternative feature points,and the statistics of the contour projection area as the feature point descriptor.Then,the ant colony algorithm is employed in matching the public feature point sequence in the two pictures.Finally,the target curve is divided by the public feature point sequence to obtain the corresponding contour curve.We use low-dimensional descriptors in the rough matching phase to increase the matching speed.Then,in the precise matching stage,Affine invariant descriptors constructed by wavelet coefficients are used to describe the target curve segment,match the 5% target with the minimum cost of the first step,obtain the matching cost of the second phase,and achieve the recognition of the affine target.Result The average retrieval rate of this algorithm is higher than that of the traditional shape projection distribution descriptor by 44.3%,The retrieval result in the MPEG-7 image library is 98.65%.The comprehensive evaluation index of precision and recall ratio on the MPEG-7 affine image library is higher than those of the traditional shape projection distribution descriptor and the shape context by 3.1% and 25%,respectively.Conclusion The main contribution of the algorithm lies in the shape projection distribution descriptor that is calculated quickly by using the contour and the wavelet affine invariant that matches the target contour sub-curve and compensates the shortcoming of the description based on the projection area distribution.Moreover,this study addresses the problems of slow calculation speed and insufficient description ability of affine descriptors,and the proposed method has a certain anti-noise ability,which can be used effectively in the field of affine shape matching and retrieval.The strict affine invariance of the QSPD descriptor ensures the applicability of this method to affine transformation shapes.However,the algorithm is not applicable for targets with large shape changes because the calculation of the QSPD is based on global shape information.