Yu Yuanlong, Mann George K I, Gosine Raymond G
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.
IEEE Trans Syst Man Cybern B Cybern. 2010 Oct;40(5):1398-412. doi: 10.1109/TSMCB.2009.2038895. Epub 2010 Feb 2.
By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.
通过扩展综合竞争假设，本文提出了一种基于对象的视觉注意力模型，该模型使用低维特征选择一个感兴趣的对象，从而使视觉感知从快速的注意力选择过程开始。所提出的注意力模型包括七个模块：存储在长期记忆（LTM）中的对象表示学习、前注意处理、自上而下的偏向、自下而上的竞争、自上而下和自下而上方式之间的调解、显著性图的生成以及感知完成处理。它分两个阶段工作：学习阶段和关注阶段。在学习阶段，当关注一个对象时，对相应的对象表示进行统计训练。提出了一种由局部编码和全局编码组成的双编码对象表示。强度、颜色和方向特征用于构建局部编码，轮廓特征用于构成全局编码。在关注阶段，模型首先使用格式塔规则将视野前注意地分割成离散的原对象。如果给出了特定任务的对象，模型从LTM中召回相应的表示，并推断出与任务相关的特征以评估自上而下的偏向。然后进行自动自下而上竞争和有意识的自上而下偏向之间的调解，以产生基于位置的显著性图。通过组合每个原对象内基于位置的显著性，评估基于原对象的显著性。选择最显著的原对象进行关注，最后将其放入感知完成处理模块以产生完整的对象区域。该模型已应用于机器人的不同任务：检测特定任务的静止和移动物体。展示了不同条件下的实验结果以验证该模型。
