Painterly depiction of material properties

Painters are masters of depiction and have learned to evoke a clear perception of materials and material attributes in a natural, three-dimensional setting, with complex lighting conditions. Furthermore, painters are not constrained by reality, meaning that they could paint materials without exactly following the laws of nature, while still evoking the perception of materials. Paintings have to our knowledge not been studied on a big scale from a material perception perspective. In this article, we studied the perception of painted materials and their attributes by using human annotations to find instances of 15 materials, such as wood, stone, fabric, etc. Participants made perceptual judgments about 30 unique segments of these materials for 10 material attributes, such as glossiness, roughness, hardness, etc. We found that participants were able to perform this task well while being highly consistent. Participants, however, did not consistently agree with each other, and the measure of consistency depended on the material attribute being perceived. Additionally, we found that material perception appears to function independently of the medium of depiction—the results of our principal component analysis agreed well with findings in former studies for photographs and computer renderings.

[1]  Edward H. Adelson,et al.  Recognizing Materials Using Perceptually Inspired Features , 2013, International Journal of Computer Vision.

[2]  A. Torralba,et al.  Specular reflections and the perception of shape. , 2004, Journal of vision.

[3]  Annamaria Giusti Art as illusion , 2009 .

[4]  Wojciech Matusik,et al.  Efficient Isotropic BRDF Measurement , 2003, Rendering Techniques.

[5]  E. Adelson,et al.  Image statistics and the perception of surface qualities , 2007, Nature.

[6]  Brian C. McCann,et al.  Decoding natural signals from the peripheral retina. , 2011, Journal of Vision.

[7]  E. Adelson,et al.  Accuracy and speed of material categorization in real-world images. , 2014, Journal of vision.

[8]  Donald P. Greenberg,et al.  Psychophysically based model of surface gloss perception , 2001, IS&T/SPIE Electronic Imaging.

[9]  R. W. Kentridge,et al.  The perception of gloss: A review , 2015, Vision Research.

[10]  Sylvia C Pont,et al.  Illusory gloss on Lambertian surfaces. , 2010, Journal of vision.

[11]  Barton L Anderson,et al.  Generative constraints on image cues for perceived gloss. , 2013, Journal of vision.

[12]  E. Adelson,et al.  Image statistics for surface reflectance perception. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  David I. Perrett,et al.  Carotenoid and melanin pigment coloration affect perceived human health , 2011 .

[14]  P. Cavanagh The artist as neuroscientist , 2005, Nature.

[15]  Bruce Walter,et al.  Looking against the light: how perception of translucency depends on lighting direction. , 2014, Journal of vision.

[16]  Edward H. Adelson,et al.  Material perception: What can you see in a brief glance? , 2010 .

[17]  Andrea J. van Doorn,et al.  Shading in the case of translucent objects , 2001, IS&T/SPIE Electronic Imaging.

[18]  Noah Snavely,et al.  OpenSurfaces , 2013, ACM Trans. Graph..

[19]  Heinrich H. Bülthoff,et al.  Perceiving translucent materials , 2004, APGV '04.

[20]  Heinrich H. Bülthoff,et al.  Low-Level Image Cues in the Perception of Translucent Materials , 2005, TAP.

[21]  I. Motoyoshi Highlight-shading relationship as a cue for the perception of translucent and transparent materials. , 2010, Journal of vision.

[22]  M. Ernst,et al.  Experience can change the 'light-from-above' prior , 2004, Nature Neuroscience.

[23]  Hans-Peter Seidel,et al.  An intuitive control space for material appearance , 2016, ACM Trans. Graph..

[24]  Nancy B. Carlisle,et al.  Where do we store the memory representations that guide attention? , 2013, Journal of vision.

[25]  Matteo Toscani,et al.  Statistical correlates of perceived gloss in natural images , 2015, Vision Research.

[26]  M. Baxandall Shadows and Enlightenment , 1995 .

[27]  E. Gombrich ART AND ILLUSION: A STUDY IN THE PSYCHOLOGY OF PICTORIAL REPRESENTATION. , 1960 .

[28]  L. Maloney,et al.  Visual Perception of Thick Transparent Materials , 2011, Psychological science.

[29]  Ana Radonjić,et al.  Color constancy supports cross-illumination color selection. , 2015, Journal of vision.

[30]  P. Green,et al.  Perceived roughness of 1/fβ noise surfaces , 2008, Vision Research.

[31]  Roland W Fleming,et al.  Real-world illumination and the perception of surface reflectance properties. , 2003, Journal of vision.

[32]  K. Gegenfurtner,et al.  Image Statistics and the Representation of Material Properties in the Visual Cortex , 2016, Front. Psychol..

[33]  Donald P. Greenberg,et al.  Toward a psychophysically-based light reflection model for image synthesis , 2000, SIGGRAPH.

[34]  Steve Marschner,et al.  A practical model for subsurface light transport , 2001, SIGGRAPH.

[35]  Sylvia C Pont,et al.  Understanding gloss perception through the lens of art: Combining perception, image analysis, and painting recipes of 17th century painted grapes. , 2019, Journal of vision.

[36]  V. Ramachandran,et al.  Transparency: Relation to Depth, Subjective Contours, Luminance, and Neon Color Spreading , 1990, Perception.

[37]  Lewis D. Griffin Partitive mixing of images: a tool for investigating pictorial perception , 1999 .

[38]  Edward H. Adelson,et al.  On seeing stuff: the perception of materials by humans and machines , 2001, IS&T/SPIE Electronic Imaging.

[39]  K. Grammer,et al.  Color homogeneity and visual perception of age, health, and attractiveness of female facial skin. , 2007, Journal of the American Academy of Dermatology.

[40]  J. Macke,et al.  Quantifying the effect of intertrial dependence on perceptual decisions. , 2014, Journal of vision.

[41]  Pascal Barla,et al.  A systematic approach to testing and predicting light-material interactions. , 2019, Journal of vision.

[42]  M. J. Van der Smagt,et al.  Distinct temporal mechanisms modulate numerosity perception. , 2019, Journal of vision.

[43]  P. Hekkert,et al.  Meanings of materials through sensorial properties and manufacturing processes , 2009 .

[44]  A. Michelson APPLICATION OF INTERFERENCE METHODS TO SPECTROSCOPIC MEASUREMENT , 1892 .

[45]  Edward H. Adelson,et al.  Speed of Material vs. Object Recognition Depends upon Viewing Condition , 2011 .

[46]  Elisa M. Tartaglia,et al.  Linking perceptual learning with identical stimuli to imagery perceptual learning. , 2015, Journal of vision.

[47]  Barton L Anderson,et al.  The dark side of gloss , 2012, Nature Neuroscience.

[48]  Barton L. Anderson,et al.  Material properties derived from three-dimensional shape representations , 2015, Vision Research.

[49]  Christiane B Wiebel,et al.  The speed and accuracy of material recognition in natural images , 2013, Attention, perception & psychophysics.

[50]  Barton L Anderson,et al.  Image statistics do not explain the perception of gloss and lightness. , 2009, Journal of vision.

[51]  R. Arnheim Art and Visual Perception, a Psychology of the Creative Eye , 1967 .

[52]  Christiane B. Wiebel,et al.  Perceptual qualities and material classes. , 2013, Journal of vision.

[53]  A. Barbot,et al.  Optical and neural anisotropy in peripheral vision , 2016, Journal of vision.

[54]  Fan Zhang,et al.  MatMix 1.0: Using optical mixing to probe visual material perception. , 2016, Journal of vision.