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Motion Estimation from Image Sequences: A Fractional Order Total Variation Model

  • Pushpendra KumarEmail author
  • Balasubramanian Raman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

In this paper, a fractional order total variation model is introduced in the estimation of motion field. In particular, the proposed model generalizes the integer order total variation models. The motion estimation is carried out in terms optical flow. The presented model is made using a quadratic and total variation terms. This mathematical formulation makes the model robust against outliers and preserves discontinuities. However, it is difficult to solve the presented model due to the non-differentiability nature of total variation term. For this purpose, the Grünwald-Letnikov derivative is used as a discretization scheme to discretize the fractional order derivative. The resulting formulation is solved by using a more efficient algorithm. Experimental results on various datasets verify the validity of the proposed model.

Keywords

Fractional derivative Image sequence Optical flow Total variation regularization 

Notes

Acknowledgements

The author, Pushpendra Kumar gratefully acknowledges the financial support provided by Council of Scientific and Industrial Research(CSIR), New Delhi, India to carry out this work.

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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of MathematicsIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Computer Science & EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

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