farneback: Optical Flow Using Farneback's Algorithm
In neuroconductor-devel-releases/Rvision: Basic Computer Vision Library

Description Usage Arguments Value Author(s) References See Also Examples

Computes a dense optical flow using the Gunnar Farneback’s algorithm.

1 2	farneback(image1, image2, pyr_scale = 0.5, levels = 3, winsize = 43, iterations = 3, poly_n = 7, poly_sigma = 1.5)

`image1`	An `Image` object.
`image2`	An `Image` object.
`pyr_scale`	Parameter, specifying the image scale (<1) to build pyramids for each image; pyr_scale = 0.5 means a classical pyramid, where each next layer is twice smaller than the previous one.
`levels`	Number of pyramid layers including the initial image; levels = 1 means that no extra layers are created and only the original images are used.
`winsize`	Averaging window size; larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field.
`iterations`	Number of iterations the algorithm does at each pyramid level.
`poly_n`	Size of the pixel neighborhood used to find polynomial expansion in each pixel; larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field, typically poly_n = 5 or 7.
`poly_sigma`	Standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for poly_n = 5, you can set poly_sigma = 1.1, for poly_n = 7, a good value would be poly_sigma = 1.5.

A matrix with the same number of rows and columns as the original images, and two layers representing the x and y components of the optical flow for each pixel of the image.

Simon Garnier, garnier@njit.edu

Farnebäck G. Two-Frame Motion Estimation Based on Polynomial Expansion. In: Bigun J, Gustavsson T, editors. Image Analysis. Springer Berlin Heidelberg; 2003. pp. 363–370. doi:10.1007/3-540-45103-X_50

plot.OF_array