Hossein Adeli

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We introduce a model of eye movements during categorical search, the task of finding and recognizing categorically defined targets. It extends a previous model of eye movements during search (target acquisition model, TAM) by using distances from an support vector machine classification boundary to create probability maps indicating pixel-by-pixel evidence(More)
In this paper, a new algorithm is proposed for solving the path planning problem of mobile robots. The algorithm is based on Artificial Potential Field (APF) methods that have been widely used for path planning related problems for more than two decades. While keeping the simplicity of traditional APF methods, our algorithm is built upon new potential(More)
Models of eye movements during scene viewing attempt to explain distributions of fixations and do not typically address the neural basis of saccade programming. Models of saccade programming are tied more closely to neural mechanisms, but have not been applied to scenes due to limitations on the inputs that they can accept-typically just dots. This work(More)
Modern computational models of attention predict fixations using saliency maps and target maps, which prioritize locations for fixation based on feature contrast and target goals, respectively. But whereas many such models are biologically plausible, none have looked to the oculomotor system for design constraints or parameter specification. Conversely,(More)
Learned region sparsity has achieved state-of-the-art performance in classification tasks by exploiting and integrating a sparse set of local information into global decisions. The underlying mechanism resembles how people sample information from an image with their eye movements when making similar decisions. In this paper we incorporate the biologically(More)
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