Modeling Metallicity: Low Level Visual Features Support Robust Material Perception (original) (raw)

Low level visual features support robust material perception in the judgement of metallicity

Scientific Reports

The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach—estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials—is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modeling that supports the use of image statistics heuristics in the judgement of metallicity—the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the exploration of low-level image statistics, whilst ensuring that each...

Image Statistics and the Fine Lines of Material Perception

i-Perception, 2016

We experience vivid percepts of objects and materials despite complexities in the way images are structured by the interaction of light with surface properties (3D shape, albedo, and gloss or specularity). Although the perception of gloss (and lightness) has been argued to depend on image statistics (e.g., sub-band skew), studies have shown that perceived gloss depends critically on the structure of luminance variations in images. Here, we found that separately adapting observers to either positive or negative skew generated declines in perceived gloss, contrary to the predictions of theories involving image statistics. We also found similar declines in perceived gloss following adaptation to contours geometrically correlated with sharp specular edges. We further found this aftereffect was stronger when contour adaptors were aligned with specular edges compared with adaptation to the same contours rotated by 90. These findings support the view that the perception of gloss depends critically on the visual system's ability to encode specular edge structure and not image skew.

Color and material perception: Achievements and challenges

Journal of Vision, 2010

There is a large literature characterizing human perception of the lightness and color of matte surfaces arranged in coplanar arrays. In the past ten years researchers have begun to examine perception of lightness and color using wider ranges of stimuli intended to better approximate the conditions of everyday viewing. One emerging line of research concerns perception of lightness and color in scenes that approximate the three-dimensional environment we live in, with objects that need not be matte or coplanar and with geometrically complex illumination. A second concerns the perception of material surface properties other than color and lightness, such as gloss or roughness. This special issue features papers that address the rich set of questions and approaches that have emerged from these new research directions. Here, we briefly describe the articles in the issue and their relation to previous work.

The influence of shape on the perception of material reflectance

ACM Transactions on Graphics, 2007

: The tesselated spheres in the left image are rendered with two different types of a blue plastic BRDF, yet they are perceived as made from the same material. The objects in the right image are rendered with an identical blue plastic BRDF, yet their appearance is very different.

The joint role of geometry and illumination on material recognition

Journal of Vision, 2021

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly non-linear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly non-linear models and humans may use similar high-level factors for material recognition tasks.

Image statistics for surface reflectance perception

Journal of the Optical Society of America A, 2008

Human observers can distinguish the albedo of real-world surfaces even when the surfaces are viewed in isolation, contrary to the Gelb effect. We sought to measure this ability and to understand the cues that might underlie it. We took photographs of complex surfaces such as stucco and asked observers to judge their diffuse reflectance by comparing them to a physical Munsell scale. Their judgments, while imperfect, were highly correlated with the true reflectance. The judgments were also highly correlated with certain image statistics, such as moment and percentile statistics of the luminance and subband histograms. When we digitally manipulated these statistics in an image, human judgments were correspondingly altered. Moreover, linear combinations of such statistics allow a machine vision system (operating within the constrained world of single surfaces) to estimate albedo with an accuracy similar to that of human observers. Taken together, these results indicate that some simple image statistics have a strong influence on the judgment of surface reflectance.

Visual Perception Modeling on Sense of Material of Object Surface

Communications in Computer and Information Science, 2013

Human can quickly recognize the state of object surface. This sensation is called "Sense of Material". When simulating materials with CG, the method of complex physical model is mainstream. However, this method causes large production time and cost so we propose simple material models based on human visual characteristics called "Tri-Contrast Perception Model" and "Binocular Parallax Model". The results of discriminant analysis to some material samples, we found that binocular parallax is important affect for sense of material.

Color perception of 3D objects: Constancy with respect to variation of surface gloss

Proceedings of the 3rd symposium on Applied …, 2006

. Reflected light varies across the surface of threedimensional objects. The left panel shows an image of a matte mug in a synthetic scene, while the right panel shows a glossy mug in the same scene. The diffuse component of the reflectance of the two mugs is identical. The colored squares at the top of each panel show the color of three individual pixels from each mug.