:   .., .., .. .
:   ࠖ
:  113
:  
:  2025
:   .., .., .. . ࠖ // . - 2025. - . 113. - .129-180.
:   ࠖ, , , ,
(.):  LucasKanade optical flow, Scharr operator, Gauss gradient operator, discrete Fourier transform, image derivative
:   ࠖ . . , . . , . , , .
(.):  This paper considers LucasKanade optical flow computation using the finite dimensional sampling theories based on Fourier transform. Such a procedure regards all image pixels for image derivative evaluation and is able to provide high accuracy of optical flow computation. This paper proposes a hybrid image differentiation method which combines the finite dimensional sampling theories with Scharr operator in order to improve accuracy of optical flow computation. Experiments on optical flow computation for real videos on the basis of the finite dimensional sampling theories as well as the hybrid method have been conducted and their results are presented. Leveraging of the finite dimensional sampling theories allows to improve accuracy of optical flow computation for videos including poor illumination and shaded regions. The research results can be applied in various computer vision tasks such as visual object tracking.

PDF

: 76, : 26, : 16.


© 2007.