Visual perception of materials and their properties
Visual perception of materials and their properties
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DOI：
10.1016/j.visres.2013.11.004
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发表时间：
2014-01-01
期刊：
影响因子：
1.8
通讯作者：
Fleming, Roland W.
中科院分区：
文献类型：
--
作者：
Fleming, Roland W.
将肥皂误认为馅饼或将肥皂误认为馅饼等错误识别材料可能会导致一些相当混乱的事故。幸运的是，我们很少遭受这样的侮辱，这在很大程度上要归功于我们出色的识别材料和肉眼识别它们的特性的能力。在日常生活中，我们遇到了各种各样的材料，我们通常毫不费力、毫无差错地辨别出来。然而，尽管材料感知主观上很容易，但它给视觉系统带来了一些独特而重大的挑战，因为给定的材料可以根据照明、视点和形状呈现多种不同的外观。在这里，我使用最近关于物质知觉的研究中的观察结果来概述物质知觉的一般理论，其中我认为视觉系统实际上并不估计物质和物体的物理参数。相反，我认为大脑非常擅长建立“统计生成模型”，以捕捉样本之间外观的自然差异程度。例如，在确定感知的光泽度时，大脑不会估计BRDF的参数。相反，它使用低级别和中级图像测量的星座来表征表面表现镜面反射的程度。我认为，这些“统计外观模型”比物理参数更有表现力，也更容易计算，因此代表了介于“诡计袋”和“逆光学”之间的一种强有力的中间方式。(C)2013年提交人。爱思唯尔有限公司出版。保留所有权利。
Misidentifying materials such as mistaking soap for pate or vice versa could lead to some pretty messy mishaps. Fortunately, we rarely suffer such indignities, thanks largely to our outstanding ability to recognize materials and identify their properties by sight. In everyday life, we encounter an enormous variety of materials, which we usually distinguish effortlessly and without error. However, despite its subjective ease, material perception poses the visual system with some unique and significant challenges, because a given material can take on many different appearances depending on the lighting, viewpoint and shape. Here, I use observations from recent research on material perception to outline a general theory of material perception, in which I suggest that the visual system does not actually estimate physical parameters of materials and objects. Instead I argue the brain is remarkably adept at building 'statistical generative models' that capture the natural degrees of variation in appearance between samples. For example, when determining perceived glossiness, the brain does not estimate parameters of the BRDF. Instead, it uses a constellation of low- and mid-level image measurements to characterize the extent to which the surface manifests specular reflections. I argue that these 'statistical appearance models' are both more expressive and easier to compute than physical parameters, and therefore represent a powerful middle way between a 'bag of tricks' and 'inverse optics'. (C) 2013 The Author. Published by Elsevier Ltd. All rights reserved.