A Neural Model Combining Attentional Orienting to Object Recognition: Preliminary Explorations on the Interplay Between Where and What
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES DEPT OF COMPUTER SCIENCE
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We propose a model of primate vision that integrates both an attentional orienting where pathway and an object recognition what pathway. The fast visual attention front-end rapidly selects the few most conspicuous image locations, and the slower object recognition back-end identifies objects at the selected locations. The model is applied to classical visual search tasks, consisting of finding a specific target among an array of distracting visual patterns e.g., a circle among many squares. The encouraging results obtained, in which substantial speedup is achieved by the combined attention- recognition model while maintaining good recognition performance compared to an exhaustive search, suggest that the biologically-inspired architecture proposed represents an efficient solution to the difficult problem of rapid scene analysis.
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