Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 10, 1434(2023)
Optical flow estimation via fusing sequence image intensity correlation information
Fig. 3. Strength-weakness correlation of the edge-region point mapping with the center-region point mapping done by the horizontal cut(The strength of the correlation is indicated by the shade of the color of the bars,The correlation factor is set to 1 when the corresponding position area is identified as strong correlation. The weaker the rest of the color,the weaker the correlation).
Fig. 4. Lookup operator based on correlation pyramid. The orange part is the neighborhood dot product of the query points at different scales.
Fig. 6. Convergence curve on KITTI-2015.(a)Convergence curve on EPE index;(b)Convergence curve on Fl index.
Fig. 7. Optical flow estimation results on the KITTI validation set(4,6,and 8 are the 4-point,6-point,and 8-point methods proposed in this paper,respectively).
Fig. 8. Convergence curve on MPI-Sintel.(a)Convergence curve on EPE index;(b)Convergence curve on 1 px index;(c)Convergence curve on 3 px index;(d)Convergence curve on 5 px index.
Fig. 9. Optical flow estimation results on the MPI- Sintel validation set(4,6,and 8 are the 4-point,6-point,and 8-point methods proposed in this paper,respectively).
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Tong AN, Di JIA, Jia-bao ZHANG, Peng CAI. Optical flow estimation via fusing sequence image intensity correlation information[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(10): 1434
Category: Research Articles
Received: Nov. 18, 2022
Accepted: --
Published Online: Oct. 25, 2023
The Author Email: Di JIA (1319423118@qq.com)