资 源 简 介
An affine (or first-order) optic flow model has 6 parameters, describing image translation, dilation, rotation and shear. The class affine_flow provides methods to estimates these parameters for two frames of an image sequence.The class implements a least-squares fit of the parameters to estimates of the spatial and temporal grey-level gradients. This is an extension of the well-known Lucas-Kanade method. The images are either sampled conventionally, on a rectilinear grid, or on a log-polar grid. In the latter case, the class may iteratively refine its estimates by moving the samplin
文 件 列 表
Contents.m
affine_flow.m
affine_flowdemo.m
affine_flowdisplay.m
affine_flowedgedisplay.m
exindex.m
gradients_xyt.m
gsmooth2.m
html
affine_flowdemo.html
affine_flowdemo.png
affine_flowdemo_01.png
affine_flowdemo_02.png
affine_flowdemo_03.png
affine_flowdemo_04.png
affine_flowdemo_05.png
affine_flowdemo_06.png
affine_flowdemo_07.png
affine_flowdemo_08.png
affine_flowdemo_09.png
imtransform_same.m
license.txt
logsampback.m
logsample.m
logtform.m
maze1.png
maze2.png
setProps.m
