Experimental efficiency analysis in robust models of spatial correlation optical flow methods under non Gaussian noisy contamination

2013 ◽  
Author(s):  
Darun Kesrarat ◽  
Vorapoj Patanavijit
Keyword(s):  
Optical Flow ◽  
Spatial Correlation ◽  
Efficiency Analysis ◽  
Non Gaussian

Algorithm Optimization of Motion Tracking Based on Optical Flow

2014 ◽  
Vol 926-930 ◽  
pp. 2938-2941
Author(s):  
Dong Ming Liu ◽  
Chao Liu ◽  
Hai Wei Mu
Keyword(s):  
Optical Flow ◽  
Motion Tracking ◽  
Differential Method ◽  
Video Image ◽  
Time Axis ◽  
Algorithm Optimization ◽  
Image Preprocessing ◽  
Smoothing Filter ◽  
Matching Filter ◽  
Non Gaussian

Optical flow is an important kind of video motion tracking algorithm, and Lucas-Kanade (LK) algorithm is an effective differential method in terms of calculating optical flow. The 3D Gaussian smoothing filter is properly introduced in the image preprocessing stage of the LK algorithm, which makes it possible to increase the correlation of the adjacent pixels in the time axis, improve the blur effect of the video image and overcome the 2D Gaussian filters disadvantage that is not suitable for the video image processing. More importantly, the optimized 3D non-Gaussian matching filter is chosen during the 3D derivative calculating, and it is capable of reducing the error rate of the velocity vector calculation and enhancing the calculation accuracy of the optical flow.

A New Spatial Correlation Model Based on the Distributed RC-∏ Model

2014 ◽  
Vol 989-994 ◽  
pp. 2204-2207
Author(s):  
Xiao Xiao Liu ◽  
Jing Bo Shao ◽  
Ling Ling Zhao
Keyword(s):  
Gaussian Process ◽  
Spatial Correlation ◽  
Process Variations ◽  
Deep Submicron ◽  
Correlation Model ◽  
Crosstalk Noise ◽  
Model Based ◽  
Reduction Techniques ◽  
Non Gaussian ◽  
Tree Branch

To solve the crosstalk noise question in deep-submicron technologies, a new spatial correlation model based on the distributed RC-π model is proposed in this paper. Quiet aggressor net and tree branch reduction techniques are introduced to the distributed RC-π model, and a new spatial correlation model of both Gaussian and non-Gaussian process variations among segments is created. Experimental results show that our method maintains the efficiency of past approaches, and significantly improves on their accuracy.

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