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L. Itti, Quantitative analysis of perceptual salience at the point of gaze in humans and monkeys, Workshop on Natural Environments, Taska, and Intelligence (NETI), Austin, Texas, Mar 2008.
Abstract: Visual processing of complex natural environments requires animals to combine, in a highly dynamic and adaptive manner, sensory signals that originate from the environment (bottom-up) with behavioral goals and priorities dictated by the task at hand (top-down). In the visual domain, bottom-up and top-down guidance of attention towards salient or behaviorally relevant targets have both been studied and modeled extensively. More recently, the interaction between bottom-up and top-down control of attention has also become of topic of interest. A number of neurally-inspired computational models have emerged which integrate components for the computation of bottom- up salience maps, top-down attention biasing, rapid computation of the 'gist' or rough context of a scene, objet recognition, and some higher-level cognitive reasoning functions. I will review a number of such efforts, which aim at building models that can both process real-world inputs in robust and flexible ways, and perform cognitive reasoning on the symbols extracted from these inputs. I will draw from examples in the biological/computer vision fields, including algorithms for complex scene understanding, robot navigation, and animation of virtual humans.
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Computational Modeling
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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