Context-free attentional operators: The generalized symmetry transform

Daniel Reisfeld*, Haim Wolfson, Yehezkel Yeshurun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 generalized many of the existing methods of 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 that 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.

Original languageEnglish
Pages (from-to)119-130
Number of pages12
JournalInternational Journal of Computer Vision
Issue number2
StatePublished - Mar 1995


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