Adaptive Non-Rigid Point Set Registration Based on Variational Bayesian

• Format: Article
• Language: zho
• Published: The Northwestern Polytechnical University 2018-10-01
• Series: Xibei Gongye Daxue Xuebao
• Subjects: non-rigid; point set registration; variational bayesian; student's t mixture model; outliers; robust
• Online Access: https://www.jnwpu.org/articles/jnwpu/pdf/2018/05/jnwpu2018365p942.pdf

For the existence of outliers in non-rigid point set registration, a method based on Bayesian student's t mixture model(SMM) is proposed. Under the framework of variational Bayesian, the point set registration problem is converted to maximize the variational lower bound of log-likelihood, where the transformation parameters are found through variational inference. By prior model, the constraint over spatial regularization is incorporated into the Bayesian SMM, which can adaptively be determined for different data sets. Compared with Gaussian distribution, the student's t distribution is more robust to outliers. The experimental comparative analysis of simulated points and real images verify the effectiveness of the proposed method on the non-rigid point set registration with outliers.