ReFlowNet: Revisiting Coarse-to-fine Learning of Optical Flow

2021 ◽  
pp. 429-442
Author(s):  
Leyang Xu ◽  
Zongqing Lu
Keyword(s):  
Author(s):  
Yong Deng ◽  
Jimin Xiao ◽  
Steven Zhiying Zhou ◽  
Jiashi Feng

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Weihua Zhang ◽  
Yi Zhang ◽  
Chaobang Gao ◽  
Jiliu Zhou

This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW) algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance without reducing accuracy is induced. The main contributions of this study include (1) a joint spatial-temporal multiresolution optical flow computation method which can keep encoding more informative motion information than recent proposed methods, (2) an enhanced DTW method to improve temporal consistence of motion in action recognition, and (3) coarse-to-fine DTW constraint on motion features pyramids to speed up recognition performance. Using this method, high recognition accuracy is achieved on different action databases like Weizmann database and KTH database.


1991 ◽  
Vol 6 (2) ◽  
pp. 133-145 ◽  
Author(s):  
Roberto Battiti ◽  
Edoardo Amaldi ◽  
Christof Koch

2013 ◽  
Vol 284-287 ◽  
pp. 1709-1714
Author(s):  
Ju Hwan Lee ◽  
Sung Min Kim

In this paper, we proposed a novel texture preserving optical flow technique to estimate the motion patterns of contrast agent on the ultrasound image. The proposed method estimated the motion fields based on three major steps. Firstly, the proposed method recomposed the original image based on the weighted structure-texture decomposition. Secondly, we applied a slightly non-convex approximation approach by utilizing the spline interpolation based coarse-to-fine warping scheme to handle the motion discontinuities in ultrasound image. Lastly, after each warping step, we employed the bilateral filter into the numerical framework to minimize the tracking errors in motion estimates. To evaluate the tracking performance of our method, we estimated the motion fields of microbubbles for the tissue mimicking phantom, and compared its results to those of the existing methods. As a result, it was found that the proposed technique provides the most reliable motion patterns of microbubbles, and reduces computational loads, simultaneously. We also confirmed the potential usefulness of our estimation scheme for the microbubble based diagnostic analysis.


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