TY - GEN
T1 - Tuning optical flow estimation with image-driven functions
AU - Nguyen, Duc Dung
AU - Jeon, Jae Wook
PY - 2011
Y1 - 2011
N2 - This paper presents a variational model to compute the optical flow using image-driven functions. The intensity, gradient and smoothness have different influences on each image area. Thus, we propose the control functions that take the image as the input to tune the estimation process. We use the second moment matrix to characterize distinct image areas and embed these functions into the variational model. We also separate the gradient term and intensity term in the model. In addition, we use the coarse-to-fine strategy to deal with the large displacement in the image sequence. Experimental results show the stability of our proposed method on different image sequences.
AB - This paper presents a variational model to compute the optical flow using image-driven functions. The intensity, gradient and smoothness have different influences on each image area. Thus, we propose the control functions that take the image as the input to tune the estimation process. We use the second moment matrix to characterize distinct image areas and embed these functions into the variational model. We also separate the gradient term and intensity term in the model. In addition, we use the coarse-to-fine strategy to deal with the large displacement in the image sequence. Experimental results show the stability of our proposed method on different image sequences.
UR - https://www.scopus.com/pages/publications/84871688078
U2 - 10.1109/ICRA.2011.5979710
DO - 10.1109/ICRA.2011.5979710
M3 - Conference contribution
AN - SCOPUS:84871688078
SN - 9781612843865
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4840
EP - 4845
BT - 2011 IEEE International Conference on Robotics and Automation, ICRA 2011
T2 - 2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Y2 - 9 May 2011 through 13 May 2011
ER -