局部线索可以将图像补丁分类为表面,物体边界或照明变化
Christopher DiMattina1,2, Eden E Sterk1,3, Madelyn G Arena1,4
1Computational Perception Laboratory, Department of Psychology, Florida Gulf Coast University, Fort Myers, FL, USA.
Journal of vision
|January 15, 2026
概括
人类视觉使用边界清晰度,纹理和亮度线索来区分阴影和遮蔽,类似于机器学习模型. 这些视觉感知线索相互作用以确定边缘分类.
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神经科学.
- 机器学习 机器学习
背景情况:
- 解析视觉场景需要检测边缘及其原因.
- 神经网络使用边界度和纹理来区分阴影和遮边缘.
研究的目的:
- 调查人类观察者是否使用类似的线索 (边界度,纹理,亮度调制) 作为机器学习模型.
- 确定这些线索在人类边缘分类中如何相互作用.
主要方法:
- 通过结合自然纹理创造了合成边缘刺激.
- 参数操作边界度,纹理调制和亮度调制.
- 人类观察者将图像分类为阴影,遮蔽或纹理.
主要成果:
- 边界度,纹理和亮度调制之间的强烈相互作用影响了分类.
- 亮度调节的影响随着边缘的度而变化:增加它有利于利边缘的遮蔽和模糊边缘的阴影.
- 边界清晰度显著影响了分类,特别是在光度调制方面.
结论:
- 人类观察者使用与机器学习模型相同的视觉线索来检测边缘和确定原因.
- 这些发现突出了人类视觉场景解析中线索的复杂相互作用.
- 一个多项逻辑回归模型有效地解释了人类的表现.
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