Variational-Bayes Optical Flow

The Horn-Schunck (HS) optical flow method is widely employed to initialize many motion estimation algorithms. In this work, a variational Bayesian approach of the HS method is presented, where the motion vectors are considered to be spatially varying Student’s t -distributed unobserved random variab...

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Bibliographic Details
Published in:Journal of mathematical imaging and vision 2014-11, Vol.50 (3), p.199-213
Main Authors: Chantas, Giannis, Gkamas, Theodosios, Nikou, Christophoros
Format: Article
Language:English
Subjects:
Applications of Mathematics
Applied sciences
Artificial intelligence
Computer Science
Computer science
control theory
systems
Exact sciences and technology
Image Processing and Computer Vision
Linear inference, regression
Mathematical Methods in Physics
Mathematics
Pattern recognition. Digital image processing. Computational geometry
Probability and statistics
Sciences and techniques of general use
Signal,Image and Speech Processing
Statistics
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Summary:The Horn-Schunck (HS) optical flow method is widely employed to initialize many motion estimation algorithms. In this work, a variational Bayesian approach of the HS method is presented, where the motion vectors are considered to be spatially varying Student’s t -distributed unobserved random variables, i.e., the prior is a multivariate Student’s t -distribution, while the only observations available is the temporal and spatial image difference. The proposed model takes into account the residual resulting from the linearization of the brightness constancy constraint by Taylor series approximation, which is also assumed to be a spatially varying Student’s t -distributed observation noise. To infer the model variables and parameters we recur to variational inference methodology leading to an expectation-maximization (EM) framework with update equations analogous to the Horn-Schunck approach. This is accomplished in a principled probabilistic framework where all of the model parameters are estimated automatically from the data. Experimental results show the improvement obtained by the proposed model which may substitute the standard algorithm in the initialization of more sophisticated optical flow schemes.
ISSN:0924-9907
1573-7683
DOI:10.1007/s10851-014-0494-3