NDLI: A Goal-Directed Visual Perception System Using Object-Based Top

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A Goal-Directed Visual Perception System Using Object-Based Top–Down Attention

Content Provider	IEEE Xplore Digital Library
Author	Yuanlong Yu Mann, G.K.I. Gosine, R.G.
Copyright Year	2009
Abstract	The selective attention mechanism is employed by humans and primates to realize a truly intelligent perception system, which has the cognitive capability of learning and thinking about how to perceive the environment autonomously. The attention mechanism involves the top-down and bottom-up ways that correspond to the goal-directed and automatic perceptual behaviors, respectively. Rather than considering the automatic perception, this paper presents an artificial system of the goal-directed visual perception by using the object-based top-down visual attention mechanism. This cognitive system can guide the perception to an object of interest according to the current task, context and learned knowledge. It consists of three successive stages: preattentive processing, top-down attentional selection and post-attentive perception. The preattentive processing stage divides the input scene into homogeneous proto-objects, one of which is then selected by the top-down attention and finally sent to the post-attentive perception stage for high-level analysis. Experimental results of target detection in the cluttered environments are shown to validate this system.
Starting Page	87
Ending Page	103
Page Count	17
File Size	2926247
File Format	PDF
ISSN	19430604
Volume Number	4
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Visualization Visual perception Feature extraction Probabilistic logic Integrated circuits Context top–down visual attention Cognitive visual perception goal-directed integrated competition hypothesis object-based visual attention
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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