In evolutionary computer vision, algorithms are usually evolved which address one particular computer vision problem. Quite often, a set of training images is used to evolve an algorithm. Another set of images is then used to evaluate the performance of those algorithms. In contrast of this standard form of algorithm evolution, it is proposed to develop a vision system which continuously evolves algorithms based on the task at hand. This adaptation of computer vision algorithms would happen on-line for every image which is presented to the system. Such a system would continuously adapt to new environmental conditions.
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Marc Ebner (2008) studied this question.
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