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F. Miau, L. Itti, A Neural Model Combining Attentional Orienting to Object Recognition: Preliminary Explorations on the Interplay Between Where and What, In: Proc. IEEE Engineering in Medicine and Biology Society (EMBS), Istanbul, Turkey, pp. 789-792, Oct 2001. (Cited by 34)
Abstract: 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.
Keywords: Visual attention ; object recognition ; scene analysis ; bottom-up ; top-down
Note: Winner of the Excellence in Neural Engineering Travel Award
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Computational Modeling, Computer Vision
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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