基于相对形状上下文与概率松弛标记法的点模式匹配算法

Point Pattern Matching Algorithm Based On Relative Shape Context and Probabilistic Relaxation Labelling

点模式匹配是计算机视觉和模式识别中重要而基础的问题。在立体视觉匹配、图像配准、目标识别与跟踪等方面都有广泛的应用,是目前各领域关注和研究的热点。该文提出了一种新的将不变特征与概率松弛标记法相结合的点模式匹配算法。该算法首先提出一种新的基于点集的不变特征一相对形状上下文,然后利用点集间相对形状上下文的统计检验匹配测度来定义概率松弛标记法中新的相容性系数,并以此为基础来构造鲁棒的支持函数。最后通过匹配概率矩阵的松弛迭代以及匹配约束条件来实现点模式匹配问题的求解。模拟仿真与真实数据实验验证了本文算法在点集间存在相似变换乃至透视变换情况下具备较高匹配正确率,而且对于噪声和出格点也具备较强的鲁棒性。

Point Pattern Matching（PPM）is an important and fundamental issue in computer vision and pattern recognition, which is widely used in stereovision,imaging registration,object recognition and tracking,etc.It is a research hot spot in such kind of fields.This paper presents a novel and robust point pattern matching algorithm in which the invariant feature and probabilistic relaxation Iabelling（PRL）are combined.A new point-set based invariant feature,Relative Shape Context（RSC）,is proposed firstly.Using the test statistic of relative shape context descriptor＇s matching scores as the foundation of new compatibility coefficients which are used in probabilistic relaxation labelling,the robust support functions are constructed based on the obtained compatibility coefficients.Finally, the correct matching results are achieved by using the relaxed iterations of matching probabilities matrix and imposing the mapping constraints required by the bijective correspondence.Experiments on both synthetic point-sets and real world data show that the proposed algorithm not only has a higher rate of correct matching under similarity or even perspective transformation between point sets,but also is robust to noise and outliers at the same time.