A neural network based on differential Gabor filters for computing image flow from two successive images

Measurements of granular flow dynamics with high speed digital images

“…Most of the current approaches for the measurement of image motion deal with 2-dimensional motions in the image plane and can be classified depending on the choice of a measurement: (1) use of brightness variations over space and time to measure instantaneous image velocities, Le., gradient-based techniques (Horn and Schunck 1981;Schunck 1989); (2) measurement of displacement of local image pattern or primitive image tokens between successive frames of a sequence, Le., correlation -based matching techniques (Burt et al 1982;Glazer et al 1983) and symbolic-token based matching techniques (Prager and &bib 1983); (3) measurement of the spatio-temporal energy of the image brightness function in a local area to determine image motion, i.e., spatio-temporal energy model (Adelson and Bergen 1985;Watson and Ahumada 1985;Heeger 1987;Daugman 1989;Tsao and Chen 1991); and (4) update of displacements based on gradient search, Le., recursive displacement estimation (Musmann et al 1985).…”

<title>Target detection using image motion error measure</title>

“…We have shown in previous work [3][4][5][6] that for a large class of fast decreasing, time-invariant, filter functions G(x,y), the effect of image motion following equation (1) on the filter response can be given as -G(x,y) * dI(xy,t) = p . vG(x,y) *I(x-ut,y-vt,t) (3) where "* " indicates convolution over the spatial region of support of G(x,y).…”

“…vG(x,y) *I(x-ut,y-vt,t) (3) where "* " indicates convolution over the spatial region of support of G(x,y).…”