基于对象的自上而下注意的目标导向视觉感知系统

Yuanlong Yu, G. Mann, R. Gosine
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引用次数: 23

摘要

人类和灵长类动物利用选择性注意机制来实现真正的智能感知系统,即具有学习和思考如何自主感知环境的认知能力。注意机制包括自顶向下和自底向上两种方式,分别对应于目标导向和自动感知行为。本文在不考虑自动感知的基础上,利用基于对象的自上而下视觉注意机制,提出了一种目标导向视觉感知的人工系统。这个认知系统可以根据当前的任务、语境和所学的知识,引导感知到感兴趣的对象。它包括三个连续的阶段:前注意加工、自上而下的注意选择和后注意感知。前注意加工阶段将输入场景划分为同质的原型对象,由自上而下的注意选择一个原型对象,最后送到后注意感知阶段进行高层次分析。实验结果验证了该系统的有效性。
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A Goal-Directed Visual Perception System Using Object-Based Top–Down Attention
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.
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来源期刊
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
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审稿时长
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Types, Locations, and Scales from Cluttered Natural Video and Actions Guest Editorial Multimodal Modeling and Analysis Informed by Brain Imaging—Part 1 Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data A Robust Gradient-Based Algorithm to Correct Bias Fields of Brain MR Images Editorial Announcing the Title Change of the IEEE Transactions on Autonomous Mental Development in 2016
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