N age Gauss-Seidel iterations k+1 5(u) I2(I(u)+l25(2)+I +12+a2 k+1 (2) L2(Im5()+l2()+1) I2+I2+a2
Nagel • Gauss-Seidel iterations
age ∈(2) I(Vu VI +2 12+12+206 y yy +I2+26)-1/2 +6-I0 Weight matrix
Nagel Weight matrix
N age Barrons implementation Spatiotemporal smoothing 4-point central differences for differentiation Velocity derivatives 1st order 2 point central difference 2 (1, 0, -1) 2nd order: cascades of 1st order derivatives
Nagel • Spatiotemporal smoothing • 4-point central differences for differentiation • Velocity derivatives – 1 st order: 2 point central difference ½ (1,0,-1) – 2 nd order: cascades of 1st order derivatives Barron’s implementation
Uras, Girosi, Verri and torre Local solution to ca(X n)+(D(x,) (x,) 0 X Solved wherever the hessian h is nonsingular 8x8 pixel regions For each region, select 8 estimates that best satisfy MVI‖<‖V‖M≡(v Choose the estimate with the smallest condition number k(h)as the velocity for the entire region
Uras, Girosi, Verri and Torre • Local solution to • Solved wherever the Hessian H is nonsingular • 8x8 pixel regions – For each region, select 8 estimates that best satisfy – Choose the estimate with the smallest condition number k(H) as the velocity for the entire region
Uras et a Barrons implementation Presmooth using Gaussian 3 pixels in space and 1.5 frames in time · Derivatives of|andv 4 point central difference operators Confidence measurement They use k(h) Barron et al. found det(h) is more reliable (H)≥1.0
Uras et al. • Presmooth using Gaussian – 3 pixels in space and 1.5 frames in time • Derivatives of I and v – 4 point central difference operators • Confidence measurement – They use k(H) – Barron et al. found det(H) is more reliable Barron’s implementation