Definition
The inverse compositional algorithm is a reformulation of the classic Lucas-Kanade algorithm to make the steepest-descent images and Hessian constant.
Background: Lucas-Kanade
The goal of the Lucas-Kanade algorithm is to minimize the sum of squared error between a template image T(x) and a warped input image I(x):
where x = (x, y)T are the pixel coordinates, W(x; p) is a parameterized set of warps, and \(\mathbf{p} = {(p_{1},\ldots p_{n})}^{\mathrm{T}}\) is a vector of parameters. The Lucas-Kanade algorithm assumes that a current estimate of p is known and then iteratively solves for increments to the parameters \(\Delta \mathbf{p}\), i.e., approximately minimize
with respect to \(\Delta \mathbf{p}\)and update the...
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Baker, S. (2014). Inverse Compositional Algorithm. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_759
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DOI: https://doi.org/10.1007/978-0-387-31439-6_759
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