Abstract
In this paper, we propose a new method based on manifold regularization technology with a thin-plate spline for image registration, which is used to remove the outlier by approximating the transformation function. Under a Bayesian framework, we use a latent variable to indicate whether a correspondence is an inlier that should satisfy a mapping function; and then, we formulate the problem as optimizing a posterior problem related to the mapping function. The initial correspondences discard some mismatching points because of the similarity constraint, which may contain important information; however, the manifold regularization (MR) term utilizes all of the feature points and preserve this information as a constraint. In addition, we use the thin-plate spline (TPS) model, which is composed of a global affine transformation and local bending function, to construct the transformation function. Finally, we obtain the solution using the expectation-maximization algorithm. Extensive experiments show that our method outperforms with other comparable state-of-art methods.
This work was supported in part by the National Natural Science Foundation of China under Grants 61771353 and 41501505, in part by Hubei Technology Innovation Project 2019AAA045, and in part by the Guangdong Provincial Department of Education 2017 “Innovation and Strong School Project” Scientific Research Project: Natural Science Characteristic Innovation Project 2017GKTSCX014.
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Dai, A., Zhou, H., Tian, Y., Zhang, Y., Lu, T. (2020). Image Registration Algorithm Based on Manifold Regularization with Thin-Plate Spline Model. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_27
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