CONTROL SYSTEMS
A. Y Ermolchev, A. N. Bugrov Determining the optimal filter for the Lucas-Kanade optical flow algorithm
DATA PROCESSING AND ANALYSIS
IMAGE PROCESSING METHODS
QUANTUM INFORMATICS
SECURITY ISSUES
A. Y Ermolchev, A. N. Bugrov Determining the optimal filter for the Lucas-Kanade optical flow algorithm

Abstract.

The work is devoted to determining of optimal filter for the Lucas-Kanade optical flow algorithm. The work shows description of Lucas-Kanade algorithm, selecting of filters from OpenCV library. Middlebury data set was selected as examples of images which were used for experiment. At the beginning filters were compared with using only on input images and then on all pyramid levels. For filters comparison average angular error and average endpoint error were used. As a result, one filtering method was selected for image filtering in Lucas-Kanade algorithm.

Keywords:

computer vision, optical flow, image filtering, Lucas-Kanade algorithm.

PP. 14-22.

DOI 10.14357/20718632190202

References

1. Horn, B.K.P., Schunk, B.G. Determining optical flow. [AI Memo 572, MIT]. 1981.
2. Lucas, B.D., Kanade, T. An iterative image registration technique with an application to stereovision. [In Proc. of the DARPA Image Understanding Workshop]. 1981.
3. Barron, J.L., Fleet, D.J., Beauchemin, S. Performance of optical flow techniques. [IJCV]. 1994.
4. Baker, S., Scharstein, D., Lewis,, J. A database and evaluation methodology for optical flow. [In Proc. ICCV]. 2007.
5. Tomasi, C., Manduchi, R. Bilateral filtering for gray and color images. [In Proc. of the Sixth International Conference on Computer Vision]. 1998.
6. Paris, S., Kornprobst, P., Tumblin, J., Durand, F. A gentle introduction to bilateral filtering and its application. [In Proc. of the Special Interest Group on Computer Graphics and Interactive Techniques Conference]. 2007.
7. A.A. Shebalov, A.N. Bazhenov. Performance study of optical flow calculation methods. [Scientific and technical statements SPbGPU]. 2012.
8. W. Prett. Digital image processing. Moscow. 1982. – 480 с.
9. I.S. Gruzman, V.S. Kirichuk, V.P. Kosyh, G.I. Peretyagin, A.A. Spektor. Digital image processing in information systems. Novosibirsk. 2002. – 352 c.
10. G.I. Vasilenko. Theory of signal recovery. Moscow. 1979. – 272 с.
11. Muddlebury dataset. Available at: http://vision.middlebury.edu/ (accessed February 17, 2019).
12. OpenCV documentation. Available at: http://docs.opencv.org/ (accessed February 17, 2019).
 

 

2019 / 03
2019 / 02
2019 / 01
2018 / 04

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".