Active vision systems, and especially foveated vision systems, depend on efficient attentional mechanisms. We propose that machine visual attention should consist of both high level, context dependent components, and low level, context free components. As a basis for the context free component, we present an attention operator based on the intuitive notion of symmetry, which generalizes many of the existing methods for detecting regions of interest. It is a low level operator that can be applied successfully without a priori knowledge of the world. The resulting symmetry edge map can be applied in various low, intermediate and high level tasks, such as extraction of interest points, grouping and object recognition. In particular, we have implemented an algorithm which locates interest points in real time, and can be incorporated in active and purposive vision systems. The results agree with some psychophysical findings concerning symmetry as well as evidence concerning selection of fixation points. We demonstrate the performance of the transform on natural, cluttered images.
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