ICTACT Journal on Image and Video Processing

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Accurate Motion Estimation for Rotational Image Sequences


Affiliations
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India
     

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Complex motion patterns, non-rigid deformations, occlusions, and illumination changes largely affect rotational image sequences. This makes accurate motion estimation a challenging task. To address this issue, we propose an optical flow model to accurately estimate motion in rotational image sequences. Our model uses an additional constraint in the objective weighted with an edge-stopping function which allows non-zero curl specifically in the regions where rotation is involved. Our implementation of the model relies upon the robust Chambolle-Pock algorithm. We discuss the effects of the model parameters and the primal-dual parameters in the convergence of the algorithm. To further validate the effectiveness of our model, we combine our algorithm with some of the sophisticated implementation approaches with weighted median filtering. Our results on some of the rotational sequences from the Middlebury benchmark datasets show that our method achieves the best average angular and end-point errors when compared with some of the well-known models in the literature.

Keywords

Motion Estimation, Primal-Dual, Rotational Motion
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  • B.K.P. Horn and B.G. Schunck, “Determining Optical Flow”, Artifical Intelligence, Vol. 17, No. 1-3, pp. 185-203, 1981.

  • G. Aubert, R. Deriche and P. Kornprobst, “Computing Optical Flow via Variational Techniques”, SIAM Journal on Applied Mathematics, Vol. 60, No. 1, pp. 156-182, 1999.

  • I. Cohen, “Nonlinear Variational Method for Optical Flow Computation”, Proceedings of the Scandinavian Conference on Image Analysis, Vol. 1, pp. 523-534, 1993.

  • A. Wedel, T. Pock, C. Zach, H. Bischof and D. Cremers, “An Improved Algorithm for TV-L1 Optical Flow”, Springer, 2009.

  • W. Hinterberger, O. Scherzer, C. Schnorr and J. Weickert, “Analysis of Optical Flow Models in the Framework of the Calculus of Variations”, Numerical Functional Analysis and Optimization, Vol. 23, No. 1-2, pp. 69-89, 2002.

  • N. Parikh and S. Boyd, “Proximal Algorithms”, Foundations and Trends in Optimization, Vol. 1, No. 3, pp. 123-231, 2013.

  • J.L. Barron, D.J. Fleet and S.S. Beauchemin, “Performance of Optical Flow Techniques”, International Journal on Computer Vision, Vol. 12, No. 1, pp. 43-77, 1994.

  • D. Sun, S. Roth and M.J. Black, “A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them”, International Journal on Computer Vision, Vol. 106, No. 2, pp. 115-137, 2014.

  • D. Sun, S. Roth and M.J. Black, “Secrets of Optical Flow Estimation and their Principles”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2432-2439, 2010.

  • Y. Li and S. Osher, “A New Median Formula with Applications to PDE based Denoising”, Communications in Mathematical Sciences, Vol. 7, No. 3, pp. 741-753, 2009.

  • A. Chambolle and T. Pock, “A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging”, Journal of Mathematical Imaging and Vision, Vol. 40, No. 1, pp. 120-145, 2011.

  • P. Perona and J. Malik, “Scale-Space and Edge Detection using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629-639, 1990.

  • H. Dirks, “Variational Methods for Joint Motion Estimation and Image Reconstruction”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-12, 2015.

  • H. Doshi and N.U. Kiran, “Nonlinear Evolutionary PDE-Based Refinement of Optical Flow”, Machine Graphics and Vision, Vol. 30, No. 1-4, pp. 45-65, 2021.

  • T. Brox, A. Bruhn, N. Papenberg and J. Weickert, “High Accuracy Optical Flow Estimation based on a Theory for Warping”, Proceedings of European Conference on Computer Vision, pp. 25-36, 2004.

  • C. Zhang, L. Ge, Z. Chen, R. Qin, M. Li and W. Liu, “Guided Filtering: Toward Edge-Preserving for Optical Flow”, IEEE Access, Vol. 6, pp. 26958-26970, 2018.

  • S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M.J. Black and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow”, International Journal on Computer Vision, Vol. 92, No. 1, pp. 1-31, 2011.

  • S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M. Black and R. Szeliski, “Middlebury Stereo Vision Page”, International Journal on Computer Vision, Vol. 88, No. 2, pp. 1-14, 2007.

  • T. Liu, “OpenOpticalFlow: An Open Source Program for Extraction of Velocity Fields from Flow Visualization Images”, Journal of Open Research Software, Vol. 5, No. 1, pp. 1-12, 2017

  • B. Liu, B. Wang and D.S. Choi, “Flow Structures of Jupiter’s Great Red Spot Extracted by using Optical Flow Method”, Physics of Fluids, Vol. 24, pp. 1-13, 2012.

  • L. Yin, R. Yang, M. Gabbouj and Y. Neuvo, “Weighted Median Filters: A Tutorial”, IEEE Transactions on Circuits and Systems-II, Analog and Digital Signal Processing, Vol. 43, No. 3, pp. 1-13, 1996.


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  • Accurate Motion Estimation for Rotational Image Sequences

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Authors

Hirak Doshi
Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India
N. Uday Kiran
Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India

Abstract


Complex motion patterns, non-rigid deformations, occlusions, and illumination changes largely affect rotational image sequences. This makes accurate motion estimation a challenging task. To address this issue, we propose an optical flow model to accurately estimate motion in rotational image sequences. Our model uses an additional constraint in the objective weighted with an edge-stopping function which allows non-zero curl specifically in the regions where rotation is involved. Our implementation of the model relies upon the robust Chambolle-Pock algorithm. We discuss the effects of the model parameters and the primal-dual parameters in the convergence of the algorithm. To further validate the effectiveness of our model, we combine our algorithm with some of the sophisticated implementation approaches with weighted median filtering. Our results on some of the rotational sequences from the Middlebury benchmark datasets show that our method achieves the best average angular and end-point errors when compared with some of the well-known models in the literature.

Keywords


Motion Estimation, Primal-Dual, Rotational Motion

References