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目标振幅和背景对比度不确定性的影响,由正常化模板匹配的观察者预测
Can Oluk1,2,3, Wilson S Geisler1,4
1Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin, TX, USA.
Journal of vision
|October 25, 2023
概括
人类视觉检测有效地处理目标振幅和背景对比度的不确定性. 对比度正常化,而不是动态的标准转移,最能解释在各种自然场景中的表现.
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.
- 信号检测理论 信号检测理论
背景情况:
- 自然视觉场景在目标属性和背景条件上同时存在不确定性.
- 了解视觉系统如何解决这些不确定性对于解释现实世界物体检测至关重要.
研究的目的:
- 在目标振幅和背景对比的同时不确定性下,研究人类在目标检测中的表现.
- 将人类的表现与理想和简化的观察者模型进行比较.
主要方法:
- 在白噪声和自然背景下测量人类对正弦波目标的检测.
- 多样化的目标先前概率和背景对比度水平.
- 将人类数据与模板匹配 (TM),规范模板匹配 (NTM) 和动态模板匹配 (DTM) 观察者进行比较.
主要成果:
- 标准化模板匹配 (NTM) 观察者与人类的表现密切匹配,特别是在低目标先前概率下.
- 动态模板匹配 (DTM) 观察者也很好地解释了人类的表现.
- 标准模板匹配 (TM) 观察者的表现很差,接近机会水平.
结论:
- 视觉系统可能利用对比度正常化来管理同时存在的不确定性.
- 对比度规范化似乎比动态标准调整更为关键,以实现可靠的目标检测.
- 高不确定性数据为区分视觉感知计算模型提供了有价值的见解.
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