Point set matching is a common problem in many domains, such as medical image analysis, object recognition, 3D reconstruction, and motion tracking. Coherent point drift (CPD) appears as an efficient algorithm to align two point sets. It treated point set matching as a problem of Gaussian mixture density estimation. But there are four drawbacks in the CPD method: outlier ratio given manually, equal prior probability for the mixture model, lack of shape information and failure for large rotation transformations. To deal with these limitations, we propose a robust CPD approach based on rotation invariant shape context. First, a rotation invariant shape context (RISC) is constructed for each point of the two sets to keep the rotation invariance of shape features. Then an adaptive prior probability and outlier ratio are computed based on RISC. For each Gaussian mixture model (GMM) component, the prior probability is linked to the number of the sample points derived from this component. Finally, the correspondence and transformation are achieved through expectation-maximization (EM) process. The results on synthetic and real data show that our method is a robust and effective non-rigid point matching approach.
['Pengpeng Zhang', 'Yu Qiao', 'Shengzheng Wang', 'Jie Yang', 'Yuemin Zhu']
A robust coherent point drift approach based on rotation invariant shape context