Abstract
Image processing is usually done by chaining a series
of well known image processing operators. Using
evolutionary methods this process may be automated. In
this paper we address the problem of evolving task
specific image processing operators. In general, the
quality of the operator depends on the task and the
current environment. Using genetic programming we
evolved an interest operator which is used to calculate
sparse optical flow. To evolve the interest operator we
define a series of criteria which need to be optimized.
The different criteria are combined into an overall
fitness function. Finally, we present experimental
results on the evolution of the interest operator.
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