在人类视觉细分中测量不确定性
Jonathan Vacher1, Claire Launay2, Pascal Mamassian1
1Laboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France.
PLoS computational biology
|September 25, 2023
概括
我们开发了一种新的方法,通过分析像素判断来绘制人类视觉细分的地图. 这种方法量化了感知细分,并为计算机视觉算法提供了基准.
科学领域:
- 视觉感知 视觉感知 视觉感知
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 视觉细分对于理解场景至关重要.
- 现有的方法缺乏人类感知量的量化措施.
- 机器学习提供算法,但缺乏对人类逻辑的洞察力.
研究的目的:
- 为测量人类感知细分地图开发一个定量范式.
- 为了使人类感知和计算模型之间的直接比较.
- 调查图像不确定性对人类细分的影响.
主要方法:
- 一种新的方法,将基于像素的相同不同判断与基于模型的重建相结合.
- 从观察自然图像和纹理的人类参与者收集感知数据.
- 分析图像不确定性如何影响个体变化和特征加权.
主要成果:
- 提出的方法成功地重建了人类细分图.
- 该方法对实验变异具有稳定性,并捕捉了个体差异.
- 图像不确定性被证明会影响人类的可变性和特征权重.
结论:
- 新的范式为研究视觉细分提供了一个定量工具.
- 它作为评估细分算法的基准.
- 它提供了对人类视觉感知背后的计算原理的见解.
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