skip to main content
research-article

Perceptual Attributes Analysis of Real-world Materials

Authors Info & Claims
Published:29 January 2019Publication History
Skip Abstract Section

Abstract

Material appearance is often represented by a bidirectional reflectance distribution function (BRDF). Although the concept of the BRDF is widely used in computer graphics and related applications, the number of actual captured BRDFs is limited due to a time and resources demanding measurement process. Several BRDF databases have already been provided publicly, yet subjective properties of underlying captured material samples, apart from single photographs, remain unavailable for users. In this article, we analyzed material samples, used in the creation of the UTIA BRDF database, in a psychophysical study with nine subjects and assessed its 12 visual, tactile, and subjective attributes. Further, we evaluated the relationship between the attributes and six material categories. We consider the presented perceptual analysis as valuable and complementary information to the database; that could aid users to select appropriate materials for their applications.

Skip Supplemental Material Section

Supplemental Material

References

  1. P. Brodatz. 1966. A Photographic Album for Artists and Designers (Brodatz Texture Database). Dover Publications.Google ScholarGoogle Scholar
  2. J. Filip, M. J. Chantler, P. R. Green, and M. Haindl. 2008. A psychophysically validated metric for bidirectional texture data reduction. ACM Trans. Graph. 27, 5 (Dec. 2008), 138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Filip and R. Vavra. 2014. Template-based sampling of anisotropic BRDFs. Comput. Graph. Forum 33, 7 (Oct. 2014), 91--99. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Filip, R. Vavra, and M. Havlicek. 2014. Effective acquisition of dense anisotropic BRDF. In Proceedings of the 22nd International Conference on Pattern Recognition (ICPR’14). 2047--2052. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Roland W. Fleming. 2014. Visual perception of materials and their properties. Vision Res. 94, 0 (2014), 62--75.Google ScholarGoogle ScholarCross RefCross Ref
  6. R. W. Fleming, R. O. Dror, and E. H. Adelson. 2003. Real-world illumination and perception of surface reflectance properties. Journal of Vision 3, 5 (2003), 347--368.Google ScholarGoogle ScholarCross RefCross Ref
  7. Roland W. Fleming, Christiane Wiebel, and Karl Gegenfurtner. 2013. Perceptual qualities and material classes. J. Vision 13, 8 (2013), 9--9.Google ScholarGoogle ScholarCross RefCross Ref
  8. Johannes Günther, Tongbo Chen, Michael Goesele, Ingo Wald, and Hans-Peter Seidel. 2005. Efficient acquisition and realistic rendering of car paint. In Proceedings of the 10th Workshop on Vision, Modeling, and Visualization (VMV’05).Google ScholarGoogle Scholar
  9. Andrew F. Hayes and Klaus Krippendorff. 2007. Answering the call for a standard reliability measure for coding data. Commun. Methods Measures 1, 1 (2007), 77--89.Google ScholarGoogle ScholarCross RefCross Ref
  10. C. Heaps and S. Handel. 1999. Similarity and features of natural textures. J. Exp. Psychol.: Human Percept. Perform. 25, 2 (1999), 299.Google ScholarGoogle ScholarCross RefCross Ref
  11. Y. X. Ho, M. S. Landy, and L. T. Maloney. 2007. Conjoint measurement of gloss and surface texture. Psychol. Sci. 19 (2007), 194--204.Google ScholarGoogle Scholar
  12. ITU. 2008. ITU-R.REC.P.910. Subjective Audivisual Quality Assessment Methods for Multimedia Applications. Technical Report.Google ScholarGoogle Scholar
  13. A. Jarabo, H. Wu, J. Dorsey, H. Rushmeier, and D. Gutierrez. 2014. Effects of approximate filtering on the appearance of bidirectional texture functions. IEEE Trans. Visual. Comput. Graph. 20, 6 (June 2014), 880--892. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. B. Keelan. 2003. ISO 20462: A psychophysical image quality measurement standard. In Proceedings of the SPIE (SPIE’03), vol. 5294. 181--189.Google ScholarGoogle Scholar
  15. B.-G. Khang, J. J. Koenderink, and A. M. L. Kappers. 2006. Perception of illumination direction in images of 3D convex objects: Influence of surface materials and light fields. Perception 35, 5 (2006), 625--645.Google ScholarGoogle ScholarCross RefCross Ref
  16. Michael S. Landy and Norma Graham. 2004. Visual perception of texture. In The Visual Neurosciences. MIT Press, 1106--1118.Google ScholarGoogle Scholar
  17. H. Long and W. K. Leow. 2002. A hybrid model for invariant and perceptual texture mapping. In Proceedings of the 16th International Conference on Pattern Recognition, Vol. 1. IEEE, 135--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Malik and P. Perona. 1990. Preattentive texture discrimination with early vision mechanisms. J. Optic. Soc. Amer. 7, 5 (1990), 923--932.Google ScholarGoogle ScholarCross RefCross Ref
  19. Stephen R. Marschner, Stephen H. Westin, Eric P. F. Lafortune, and Kenneth E. Torrance. 2000. Image-based bidirectional reflectance distribution function measurement. Appl. Opt. 39, 16 (June 2000), 2592--2600.Google ScholarGoogle ScholarCross RefCross Ref
  20. Rodrigo Martín, Julian Iseringhausen, Michael Weinmann, and Matthias B. Hullin. 2015. Multimodal perception of material properties. In Proceedings of the ACM SIGGRAPH Symposium on Applied Perception (SAP’15). 33--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. Matusik, H. Pfister, M. Brand, and L. McMillan. 2003. A data-driven reflectance model. ACM Trans. Graph. 22, 3 (2003), 759--769. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. Mojsilovic, J. Kovacevic, D. Kall, R. J. Safranek, and S. Kicha Ganapathy. 2000. The vocabulary and grammar of color patterns. IEEE Trans. Image Process. 9, 3 (2000), 417--431. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. I. Motoyoshi, S. Nishida, L. Sharan, and E. H. Adelson. 2007. Image statistics and the perception of surface qualities. Nature 447, 10 (2007), 206--209.Google ScholarGoogle ScholarCross RefCross Ref
  24. A. Ngan, F. Durand, and W. Matusik. 2005. Experimental analysis of BRDF models. Eurograp. Symp. Render. 2 (2005), 117--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. F. E. Nicodemus, J. C. Richmond, J. J. Hsia, I. W. Ginsburg, and T. Limperis. 1977. Geometrical considerations and nomenclature for reflectance. NBS Monograph 160 (1977), 1--52.Google ScholarGoogle Scholar
  26. S. Padilla, O. Drbohlav, P. R. Green, A. Spence, and M. J. Chantler. 2008. Perceived roughness of 1/f<sup>β</sup> noise surfaces. Vision Res. 48, 17 (2008), 1791--1797.Google ScholarGoogle ScholarCross RefCross Ref
  27. S. C. Pont, P. Sen, and P. Hanrahan. 2007. 2½D texture mapping: Real-time perceptual surface roughening. In Proceedings of the 4th Symposium on Applied Perception in Graphics and Visualization. 69--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. G. Ramanarayanan, J. Ferwerda, B. Walter, and K. Bala. 2007. Visual equivalence: Towards a new standard for image fidelity. ACM Trans. Graph. 26, 3 (2007), 76:1--76:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. A. Ravishankar Rao and G. L. Lohse. 1996. Towards a texture naming system: Identifying relevant dimensions of texture. Vision Res. 36, 11 (1996), 1649--1669.Google ScholarGoogle ScholarCross RefCross Ref
  30. G. Schwartz and K. Nishino. 2013. Visual material traits: Recognizing per-pixel material context. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 883--890. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. G. Schwartz and K. Nishino. 2015. Automatically discovering local visual material attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 3565--3573.Google ScholarGoogle Scholar
  32. Ana Serrano, Diego Gutierrez, Karol Myszkowski, Hans-Peter Seidel, and Belen Masia. 2016. An intuitive control space for material appearance. ACM Trans. Graph. 35, 6, Article 186 (Nov. 2016). Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Lavanya Sharan, Ce Liu, Ruth Rosenholtz, and Edward H. Adelson. 2013. Recognizing materials using perceptually inspired features. Int. J. Comput. Vision 103, 3 (July 2013), 348--371.Google ScholarGoogle ScholarCross RefCross Ref
  34. H. Tamura, S. Mori, and T. Yamawaki. 1978. Textural features corresponding to visual perception. IEEE Trans. Syst., Man Cybernet. 8, 6 (1978), 460--473.Google ScholarGoogle ScholarCross RefCross Ref
  35. Midori Tanaka and Takahiko Horiuchi. 2015. Investigating perceptual qualities of static surface appearance using real materials and displayed images. Vision Res. 115 (2015), 246--258.Google ScholarGoogle ScholarCross RefCross Ref
  36. S. F. te Pas and S. C. Pont. 2005. A comparison of material and illumination discrimination performance for real rough, real smooth and computer generated smooth spheres. In Proceedings of the 2nd Symposium on Applied Perception in Graphics and Visualization. 57--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. S. F. te Pas and S. C. Pont. 2005. Estimations of light-source direction depend critically on material BRDFs. Percept., ECVP Abstract Suppl. 34 (2005), 212.Google ScholarGoogle Scholar
  38. P. Vangorp, J. Laurijssen, and P. Dutre. 2007. The influence of shape on the perception of material reflectance. ACM Trans. Graph. 26, 3 (2007), 77:1--77:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. M. Vanrell and J. Vitria. 1997. A four-dimensional texture representation space. In Pattern Recognition and Image Analysis, Vol. 1. 245--250.Google ScholarGoogle Scholar
  40. M. Vanrell, J. Vitria, and X. Roca. 1997. A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Mach. Vision Appl. 9, 5/6 (1997), 262--271. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Josh Wills, Sameer Agarwal, David Kriegman, and Serge Belongie. 2009. Toward a perceptual space for gloss. ACM Trans. Graph. 28, 4, Article 103 (Sept. 2009), 15 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Perceptual Attributes Analysis of Real-world Materials

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on Applied Perception
        ACM Transactions on Applied Perception  Volume 16, Issue 1
        January 2019
        104 pages
        ISSN:1544-3558
        EISSN:1544-3965
        DOI:10.1145/3310277
        Issue’s Table of Contents

        Copyright © 2019 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 January 2019
        • Revised: 1 September 2018
        • Accepted: 1 September 2018
        • Received: 1 August 2017
        Published in tap Volume 16, Issue 1

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format