Depth perception in disparity-defined objects: finding the balance between averaging and segregation.
Depth perception in disparity-defined objects: finding the balance between averaging and segregation.
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DOI：
10.1098/rstb.2015.0258
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发表时间：
2016-06-19
期刊：
影响因子：
--
通讯作者：
Harris JM
中科院分区：
文献类型：
--
作者：
Cammack P;Harris JM
决定什么构成一个物体，什么背景，是视觉系统的一项基本任务。这提出了一个难题：需要在视觉场景上求平均，以获得用于对象分离的精确信号，但是需要分离来定义应当进行平均的区域。通过双眼视差（双眼视图之间的差异）获得的深度可以通过深度差异识别物体和背景来帮助分离。在这里，我们探讨深度知觉的清晰度定义的对象。我们表明，一个简单的对象隔离规则，然后在隔离区域平均，可以解释深度估计误差。为此，我们将具有平滑变化的深度边缘的对象与具有尖锐深度边缘的对象进行比较，发现前者的感知峰值深度降低。计算模型使用基于对象形状的规则对对象的中心部分进行隔离和平均，并且能够模拟感知深度的减少。我们还证明了隔离区域不是预定义的，而是取决于对象形状。我们将讨论如何隔离策略可以采用动物寻求阻止双目捕食者。这篇文章是“三维世界的视觉”专题的一部分。
Deciding what constitutes an object, and what background, is an essential task for the visual system. This presents a conundrum: averaging over the visual scene is required to obtain a precise signal for object segregation, but segregation is required to define the region over which averaging should take place. Depth, obtained via binocular disparity (the differences between two eyes’ views), could help with segregation by enabling identification of object and background via differences in depth. Here, we explore depth perception in disparity-defined objects. We show that a simple object segregation rule, followed by averaging over that segregated area, can account for depth estimation errors. To do this, we compared objects with smoothly varying depth edges to those with sharp depth edges, and found that perceived peak depth was reduced for the former. A computational model used a rule based on object shape to segregate and average over a central portion of the object, and was able to emulate the reduction in perceived depth. We also demonstrated that the segregated area is not predefined but is dependent on the object shape. We discuss how this segregation strategy could be employed by animals seeking to deter binocular predators. This article is part of the themed issue ‘Vision in our three-dimensional world’.