Institute of Neuroscience, Henry Wellcome Building, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK.
Institute of Neuroscience, Henry Wellcome Building, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK.
Curr Biol. 2018 Feb 19;28(4):588-593.e4. doi: 10.1016/j.cub.2018.01.012. Epub 2018 Feb 8.
Stereopsis is the ability to estimate distance based on the different views seen in the two eyes [1-5]. It is an important model perceptual system in neuroscience and a major area of machine vision. Mammalian, avian, and almost all machine stereo algorithms look for similarities between the luminance-defined images in the two eyes, using a series of computations to produce a map showing how depth varies across the scene [3, 4, 6-14]. Stereopsis has also evolved in at least one invertebrate, the praying mantis [15-17]. Mantis stereopsis is presumed to be simpler than vertebrates' [15, 18], but little is currently known about the underlying computations. Here, we show that mantis stereopsis uses a fundamentally different computational algorithm from vertebrate stereopsis-rather than comparing luminance in the two eyes' images directly, mantis stereopsis looks for regions of the images where luminance is changing. Thus, while there is no evidence that mantis stereopsis works at all with static images, it successfully reveals the distance to a moving target even in complex visual scenes with targets that are perfectly camouflaged against the background in terms of texture. Strikingly, these insects outperform human observers at judging stereoscopic distance when the pattern of luminance in the two eyes does not match. Insect stereopsis has thus evolved to be computationally efficient while being robust to poor image resolution and to discrepancies in the pattern of luminance between the two eyes. VIDEO ABSTRACT.
立体视是基于双眼所看到的不同视角来估计距离的能力[1-5]。它是神经科学中重要的模型感知系统，也是机器视觉的主要领域。哺乳动物、鸟类和几乎所有的机器立体算法都在寻找两只眼睛的亮度定义图像之间的相似性，通过一系列计算来生成一张图，显示场景中深度的变化[3,4,6-14]。至少有一种无脊椎动物，螳螂，也进化出了立体视[15-17]。螳螂的立体视被认为比脊椎动物的简单[15,18]，但目前对其基础计算知之甚少。在这里，我们表明，螳螂的立体视使用了一种与脊椎动物立体视根本不同的计算算法——它不是直接比较两只眼睛图像中的亮度，而是寻找图像中亮度变化的区域。因此，虽然没有证据表明螳螂的立体视可以处理静态图像，但它可以成功地揭示移动目标的距离，即使在目标与背景在纹理上完全匹配的复杂视觉场景中也是如此。引人注目的是，当两只眼睛的亮度模式不匹配时，这些昆虫在判断立体视距方面的表现优于人类观察者。昆虫的立体视因此进化为在计算上高效，同时对图像分辨率差和两只眼睛之间亮度模式的差异具有鲁棒性。视频摘要。
