The Coherent Point Drift for Clustered Point Sets
This is an incremental improvement for computer vision and digital medicine tasks, enhancing registration accuracy in specific domains.
The paper tackles non-rigid point set registration by incorporating prior clustering information to limit solution possibilities, resulting in improved accuracy with minimal performance loss, as demonstrated in digital medicine applications like heart model personalization.
The problem of non-rigid point set registration is a key problem for many computer vision tasks. In many cases the nature of the data or capabilities of the point detection algorithms can give us some prior information on point sets distribution. In non-rigid case this information is able to drastically improve registration results by limiting number of possible solutions. In this paper we explore use of prior information about point sets clustering, such information can be obtained with preliminary segmentation. We extend existing probabilistic framework for fitting two level Gaussian mixture model and derive closed form solution for maximization step of the EM algorithm. This enables us to improve method accuracy with almost no performance loss. We evaluate our approach and compare the Cluster Coherent Point Drift with other existing non-rigid point set registration methods and show it's advantages for digital medicine tasks, especially for heart template model personalization using patient's medical data.