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Update name from Gunner to Gunnar as that's the name he published his

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paper under.
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Jonathan Dönszelmann 2021-12-11 08:03:10 +01:00 committed by jonay2000
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doc/js_tutorials/js_video/js_lucas_kanade/js_lucas_kanade.markdown
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@ -133,9 +133,9 @@ Dense Optical Flow in OpenCV.js
Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected
using Shi-Tomasi algorithm). OpenCV.js provides another algorithm to find the dense optical flow. It
computes the optical flow for all the points in the frame. It is based on Gunner Farneback's
computes the optical flow for all the points in the frame. It is based on Gunnar Farneback's
algorithm which is explained in "Two-Frame Motion Estimation Based on Polynomial Expansion" by
Gunner Farneback in 2003.
Gunnar Farneback in 2003.
We use the function: **cv.calcOpticalFlowFarneback (prev, next, flow, pyrScale, levels, winsize,
iterations, polyN, polySigma, flags)**

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doc/tutorials/video/optical_flow/optical_flow.markdown
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@ -136,9 +136,9 @@ Dense Optical Flow in OpenCV
Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected
using Shi-Tomasi algorithm). OpenCV provides another algorithm to find the dense optical flow. It
computes the optical flow for all the points in the frame. It is based on Gunner Farneback's
computes the optical flow for all the points in the frame. It is based on Gunnar Farneback's
algorithm which is explained in "Two-Frame Motion Estimation Based on Polynomial Expansion" by
Gunner Farneback in 2003.
Gunnar Farneback in 2003.
Below sample shows how to find the dense optical flow using above algorithm. We get a 2-channel
array with optical flow vectors, \f$(u,v)\f$. We find their magnitude and direction. We color code the