Discovering hierarchical motion structure
Discovering hierarchical motion structure
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DOI：
10.1016/j.visres.2015.03.004
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发表时间：
2016-09-01
期刊：
影响因子：
1.8
通讯作者：
Jaekel, Frank
中科院分区：
文献类型：
--
作者：
Gershman, Samuel J.;Tenenbaum, Joshua B.;Jaekel, Frank
充满移动对象的场景通常是分层组织的：迁徙的鹅的运动嵌套在其羊群的飞行模式中，汽车的运动嵌套在路上其他汽车的交通模式中，身体部位的运动嵌套在身体的运动中。即使在有两个或三个移动点的刺激中，人类也能感知到层次结构。一个有影响力的层次运动感知理论认为，视觉系统对运动物体进行“矢量分析”，将它们分解为共同运动和相对运动。然而，该理论并没有规定如何解决歧义时，一个场景承认一个以上的矢量分析。我们描述了贝叶斯理论的矢量分析，并表明它可以解释经典的结果从点运动实验，以及新的实验数据。我们的理论朝着理解移动场景如何被解析为对象迈出了一步。(C)2015爱思唯尔有限公司版权所有。
Scenes filled with moving objects are often hierarchically organized: the motion of a migrating goose is nested within the flight pattern of its flock, the motion of a car is nested within the traffic pattern of other cars on the road, the motion of body parts are nested in the motion of the body. Humans perceive hierarchical structure even in stimuli with two or three moving dots. An influential theory of hierarchical motion perception holds that the visual system performs a "vector analysis" of moving objects, decomposing them into common and relative motions. However, this theory does not specify how to resolve ambiguity when a scene admits more than one vector analysis. We describe a Bayesian theory of vector analysis and show that it can account for classic results from dot motion experiments, as well as new experimental data. Our theory takes a step towards understanding how moving scenes are parsed into objects. (C) 2015 Elsevier Ltd. All rights reserved.