SideEye：一个基于生成式神经网络的人类周边视觉模拟器
SideEye: A Generative Neural Network Based Simulator of Human Peripheral Vision
摘要：
中央凹视力占视野的不到1%。另外99%是周边视觉。人类在外围看到的东西既明显又神秘，因为我们用自己的眼睛看到它，但除了在受控的实验室实验中，我们无法想象我们看到的东西。周边信息的退化远比径向模糊所能模拟的要复杂得多。相反，行为验证的模型假设，周边视觉测量大量的局部纹理统计在池区域重叠和偏心增长。在这项工作中，我们开发了一种新的方法，周边视觉模拟训练生成神经网络的行为验证的全场合成模型。通过实现运行时间减少21，000倍，我们的方法是第一个将周边视觉模拟的真实性和速度结合起来的方法，提供了一种全新的视觉设计方法：通过周边可视化。
英文摘要：
Foveal vision makes up less than 1% of the visual field. The other 99% is peripheral vision. Precisely what human beings see in the periphery is both obvious and mysterious in that we see it with our own eyes but can't visualize what we see, except in controlled lab experiments. Degradation of information in the periphery is far more complex than what might be mimicked with a radial blur. Rather, behaviorally-validated models hypothesize that peripheral vision measures a large number of local texture statistics in pooling regions that overlap and grow with eccentricity. In this work, we develop a new method for peripheral vision simulation by training a generative neural network on a behaviorally-validated full-field synthesis model. By achieving a 21,000 fold reduction in running time, our approach is the first to combine realism and speed of peripheral vision simulation to a degree that provides a whole new way to approach visual design: through peripheral visualization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
