MODELING VISUAL-ATTENTION VIA SELECTIVE TUNING
MODELING VISUAL-ATTENTION VIA SELECTIVE TUNING
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DOI：
10.1016/0004-3702(95)00025-9
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发表时间：
1995-10-01
影响因子：
14.4
通讯作者：
NUFLO, F
中科院分区：
文献类型：
--
作者：
TSOTSOS, JK;CULHANE, SM;NUFLO, F
基于选择性调谐的概念，提出了视觉注意各方面的模型。它提供了一种解决在图像中选择、通过视觉处理层级传递信息以及任务特定注意偏向的问题的解决方案。中心论点是，注意力的作用是优化视觉解决方案中固有的搜索过程。它通过有选择地调整视觉处理网络来实现这一点，这是通过嵌入在视觉处理金字塔中的自上而下的赢家通吃过程的层次结构来完成的。与注意力的其他主要计算模型和相关神经生物学的比较在整篇论文中都有详细的介绍。该模型已被实现，并给出了几个性能实例。这个模型是关于灵长类视觉注意的假设，但它也比机器视觉中现有的注意力计算解决方案更好，非常适合于解决机器人视觉系统中的问题。
A model for aspects of visual attention based on the concept of selective tuning is presented. It provides for a solution to the problems of selection in an image, information routing through the visual processing hierarchy and task-specific attentional bias. The central thesis is that attention acts to optimize the search procedure inherent in a solution to vision. It does so by selectively tuning the visual processing network which is accomplished by a top-down hierarchy of winner-take-all processes embedded within the visual processing pyramid. Comparisons to other major computational models of attention and to the relevant neurobiology are included in detail throughout the paper. The model has been implemented; several examples of its performance are shown. This model is a hypothesis for primate visual attention, but it also outperforms existing computational solutions for attention in machine vision and is highly appropriate to solving the problem in a robot vision system.