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Paper ThA114.1

Nguyen, Duc Dung (Sungkyunkwan University), Jeon, Jae Wook (Sungkyunkwan Univ.)

Tuning Optical Flow Estimation with Image-Driven Functions

Scheduled for presentation during the Regular Sessions "Computer Vision I: Model" (ThA114), Thursday, May 12, 2011, 08:20−08:35, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on May 23, 2019

Keywords Computer Vision for Robotics and Automation, Learning and Adaptive Systems, Motion and Path Planning

Abstract

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. These results can be used in various applications on robot such as object detection, human segmentation, etc.

 

 

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