Experimental Analysis of Non-Gaussian Noise Resistance on Global Method Optical Flow using Bilateral in Reverse Confidential

This paper presents the analytical of non-Gaussian noise resistance with the aid of the use of bilateral in reverse confidential with the optical flow. In particular, optical flow is the sample of the image’s motion from the consecutive images caused by the object’s movement. It is a 2-D vector where every vector is a displacement vector displaying the motion from the first image to the second. When the noise interferes with the image flow, the approximated performance on the vector in optical flow is poor. We ensure greater appropriate noise resistance by applying bilateral in reverse confidential in optical flow in the experiment by concerning the Error Vector Magnitude (EVM). Many noise resistance models of the global method optical flow are using for comparison in our experiment. And many sequenced image data sets where they are interfered with by several types of non-Gaussian noise are used for experimental analysis

[1]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[2]  Syed Abdul Rahman Al-Haddad,et al.  Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal , 2018 .

[3]  Lanani Abderrahim,et al.  Novel design of a fractional wavelet and its application to image denoising , 2020 .

[4]  Vorapoj Patanavijit,et al.  Experimental Analysis of Performance Comparison on Both Linear Filter and Bidirectional Condential Technique for Spatial Domain Optical Flow Algorithm , 1970 .

[5]  Mikhail Mozerov.  Constrained Optical Flow Estimation as a Matching Problem , 2013, IEEE Transactions on Image Processing.

[6]  C.-C. Jay Kuo,et al.  Tree-structured multi-stage principal component analysis (TMPCA): Theory and applications , 2018, Expert Syst. Appl..

[7]  John Radford,et al.  Thinking in perspective: Critical essays in the study of thought processes , 1978 .

[8]  Vorapoj Patanavijit,et al.  Robust global based spatial correlation optical flow in bidirectional confidential technique with bilateral filter , 2015, 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

[9]  Todd Randall Reed,et al.  Digital image sequence processing, compression, and analysis , 2004 .

[10]  David J. Fleet,et al.  Optical Flow Estimation , 2006, Handbook of Mathematical Models in Computer Vision.

[11]  David H. Warren,et al.  Electronic spatial sensing for the blind : contributions from perception, rehabilitation, and computer vision , 1985 .

[12]  Roshidi Din,et al.  Review on techniques and file formats of image compression , 2020 .

[13]  Feng Wu,et al.  Learning for Video Compression , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Songyu Yu,et al.  Confidence based optical flow algorithm for high reliability , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[16]  Jian Sun,et al.  Symmetric stereo matching for occlusion handling , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Sidi Ahmed Mahmoudi,et al.  Real-time motion tracking using optical flow on multiple GPUs , 2014 .

[18]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[19]  C.-C. Jay Kuo,et al.  Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[20]  David J. Fleet,et al.  Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.

[21]  Vorapoj Patanavijit,et al.  A novel elementary spatial expanding scheme form on SISR method with modifying geman & mcclure function , 2019 .