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Adaptive mixed norm optical flow estimation
- Source :
- Visual Communications and Image Processing (VCIP 2005), Proceedings of the SPIE
- Publication Year :
- 2016
-
Abstract
- The pel-recursive computation of 2-D optical flow has been extensively studied in computer vision to estimate motion from image sequences, but it still raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. It relies on spatio-temporal brightness variations due to motion. Our proposed adaptive regularized approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Mixed Norm (MN) to estimate the best motion vector for a given pixel. In our model, various types of noise can be handled, representing different sources of error. The motion vector estimation takes into consideration local image properties and it results from the minimization of a mixed norm functional with a regularization parameter depending on the kurtosis. This parameter determines the relative importance of the fourth norm and makes the functional convex. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Experiments indicate that this approach provides robust estimates of the optical flow.<br />8 pages, 4 figures. arXiv admin note: text overlap with arXiv:1403.7365
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Optical flow
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Computer Science - Computer Vision and Pattern Recognition
Image processing
Motion vector
Optical flow estimation
Norm (mathematics)
Motion estimation
Outlier
Kurtosis
Algorithm
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Visual Communications and Image Processing (VCIP 2005), Proceedings of the SPIE
- Accession number :
- edsair.doi.dedup.....81e49b72c53925a16922b9721c551b49