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Image alignment using mixture models for discontinuous deformations
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AI
DOI：10.1016/j.sigpro.2022.108467.png)
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Abstract
En 中文
Image alignment is a challenging problem for the cases when objects in the scene appear discontinuous displacements in images captured from different view points. In this paper, a mixture model approach based on mesh warping is proposed. Given a set of corresponding feature points between images, variational Bayesian linear regression framework is adopted to estimate mixture models and determine the number of components. To improve the alignment accuracy, warping model of each component is refined separately by minimizing the photometric error. Then, all pixels in the overlapping region are assigned to one of the components in terms of the photometric similarity, the distribution of feature points and the consistency of adjacent pixels, which contributes to the final prediction of warping. Experiment demonstrates that the proposed method is effective on real data and achieves higher alignment accuracy than state-of-the-arts.(c) 2022 Published by Elsevier B.V.
Keywords:
Image alignment
Discontinuous deformation
Mesh warping
Mixture models
Variational Bayesian
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