Attention links sensing to recognition
This paper presents arguments that explicit strategies for visual attentional selection are important for cognitive vision systems, and shows that a number of proposals currently exist for exactly how parts of this goal may be accomplished. A comprehensive survey of approaches to computational attention is given. A key characteristic of virtually all the models surveyed here is that they receive significant inspiration from the neurobiology and psychophysics of human and primate vision. This, although not necessarily a key component of mainstream computer vision, seems very appropriate for cognitive vision systems given a definition of the topic that always includes the goal of human-like visual performance. A particular model, the Selective Tuning model, is overviewed in some detail. The growing neurobiological and psychophysical evidence for its biological plausibility is cited highlighting the fact that it has more biological support than other models; it is further claimed that it may form an appropriate starting point for the difficult task of integrating attention into cognitive vision systems.
attention recognition cognitive vision selective tuning