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Optical flow can be estimated in a number of ways. Broadly, optical flow estimation approaches can be divided into machine learning based models (sometimes called data-driven models), classical models (sometimes called knowledge-driven models) which do not use machine learning and hybrid models which use aspects of both learning based models and classical models.
Motion interpolation is a programming technique in data-driven character animation that creates transitions between example motions and extrapolates new motions. Example motions are often created through keyframing or motion capture. However, keyframing is labor-intensive and lacks varieties of motion, and both processes result in motions that ...
In computer graphics, slerp is shorthand for spherical linear interpolation, introduced by Ken Shoemake [1] in the context of quaternion interpolation for the purpose of animating 3D rotation. It refers to constant-speed motion along a unit-radius great circle arc, given the ends and an interpolation parameter between 0 and 1.
A plot of the condition number by the shape parameter for a 15x15 radial basis function interpolation matrix using the Gaussian On the opposite side of the spectrum, the condition number of the interpolation matrix will diverge to infinity as ε → 0 {\displaystyle \varepsilon \to 0} leading to ill-conditioning of the system.
Smoothstep is a family of sigmoid-like interpolation and clamping functions commonly used in computer graphics, [1] [2] video game engines, [3] and machine learning. [ 4 ] The function depends on three parameters, the input x , the "left edge" and the "right edge", with the left edge being assumed smaller than the right edge.
In computer vision, the Lucas–Kanade method is a widely used differential method for optical flow estimation developed by Bruce D. Lucas and Takeo Kanade.It assumes that the flow is essentially constant in a local neighbourhood of the pixel under consideration, and solves the basic optical flow equations for all the pixels in that neighbourhood, by the least squares criterion.
The next figure shows the interpolation through four points (marked by "circles") using different types of polyharmonic splines. The "curvature" of the interpolated curves grows with the order of the spline and the extrapolation at the left boundary ( x < 0) is reasonable.
This process yields p 0,4 (x), the value of the polynomial going through the n + 1 data points (x i, y i) at the point x. This algorithm needs O(n 2) floating point operations to interpolate a single point, and O(n 3) floating point operations to interpolate a polynomial of degree n.