Abstract
No feature-based vision system can work unless good features can
be identified and tracked from frame to frame. Although tracking itself
is by and large a solved problem, selecting features that can be tracked
well and correspond to physical points in the world is still hard. We
propose a feature selection criterion that is optimal by construction
because it is based on how the tracker works, and a feature monitoring
method that can detect occlusions, disocclusions, and features that do
not correspond to points in the world. These methods are based on a new
tracking algorithm that extends previous Newton-Raphson style search
methods to work under affine image transformations. We test performance
with several simulations and experiments
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