纹理总结统计在材料识别图纸和照片中的作用
Benjamin Balas1,2, Michelle R Greene3,4
1Psychology Department, North Dakota State University, Fargo, ND, USA.
Journal of vision
|December 8, 2023
概括
这项研究揭示了纹理统计数据对照片与图纸中的材料识别的影响不同. 深层卷积神经网络不能完全捕捉人类对绘制材料的感知.
科学领域:
- 视觉感知 视觉感知 视觉感知
- 计算机视觉 计算机视觉 计算机视觉
- 艺术化染 艺术化染
背景情况:
- 艺术中的材料描绘提供了对支持材料分类的视觉特征的洞察力.
- 了解物质感知需要分析视觉特征如何影响不同图像格式的识别.
研究的目的:
- 研究如何从照片和图纸上的材料识别受到纹理合成的影响.
- 将人类的表现与深层卷积神经网络 (CNN) 分类准确度进行比较.
主要方法:
- 应用了Portilla-Simoncelli纹理合成模型来操纵外观总结统计数据.
- 他比较了人类观察者从合成图像的材料分类与预训练有素的CNN.
- 分析了材料和图像格式 (照片与图纸) 的性能差异.
主要成果:
- 纹理统计在材料分类中起到不同的作用,取决于染格式和材料类型.
- 人类和CNN的表现显示了摄影图像的相似之处,但在绘图方面存在显著差异.
- 训练有素的物体识别CNN不能有效地捕捉人类对材料图纸的感知.
结论:
- 人类对物质图纸的感知没有得到当前深层卷积神经网络的充分代表.
- 纹理统计在不同视觉介质的材料分类中是至关重要的,但却是可变的因素.
- 需要进一步的研究来开发能够更好地考虑艺术染中人类感知的模型.
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