1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Vision Science
  4. Volume 3, 2017
  5. Article

Annual Review of Vision Science

Volume 3, 2017

Textures as Probes of Visual Processing

Abstract

Visual textures are a class of stimuli with properties that make them well suited for addressing general questions about visual function at the levels of behavior and neural mechanism. They have structure across multiple spatial scales, they put the focus on the inferential nature of visual processing, and they help bridge the gap between stimuli that are analytically convenient and the complex, naturalistic stimuli that have the greatest biological relevance. Key questions that are well suited for analysis via visual textures include the nature and structure of perceptual spaces, modulation of early visual processing by task, and the transformation of sensory stimuli into patterns of population activity that are relevant to perception.

Keyword(s): image statistics, perceptual spaces, segmentation, spatial vision, top-down modulation, visual mechanisms
Loading

Article metrics loading...

/content/journals/10.1146/annurev-vision-102016-061316
2017-09-15
2024-05-03
Loading full text...

Full text loading...

/deliver/fulltext/vision/3/1/annurev-vision-102016-061316.html?itemId=/content/journals/10.1146/annurev-vision-102016-061316&mimeType=html&fmt=ahah

Literature Cited

  1. Adelson EH. 2001. On seeing stuff: the perception of materials by humans and machines. Proc. SPIE 4299, Hum. Vis. Electron. Imaging VI, San Jose, CA, Jan. 201–12
  2. Amari S-I. 2001. Information geometry on hierarchy of probability distributions. IEEE Trans. Inf. Theory 47:1701–11 [Google Scholar]
  3. Balas B, Nakano L, Rosenholtz R. 2009. A summary-statistic representation in peripheral vision explains visual crowding. J. Vis. 9:1213 [Google Scholar]
  4. Balasubramanian V, Sterling P. 2009. Receptive fields and functional architecture in the retina. J. Physiol. 587:2753–67 [Google Scholar]
  5. Barbosa MS, Bubna-Litic A, Maddess T. 2013. Locally countable properties and the perceptual salience of textures. J. Opt. Soc. Am. A 30:1687–97 [Google Scholar]
  6. Bar-Joseph Z, El-Yaniv R, Lischinski D, Werman M. 2001. Texture mixing and texture movie synthesis using statistical learning. IEEE Trans. Vis. Comput. Graph. 7:120–35 [Google Scholar]
  7. Beason-Held LL, Purpura KP, Krasuski JS, Desmond RE, Mangot DJ. et al. 2000. Striate cortex in humans demonstrates the relationship between activation and variations in visual form. Exp. Brain Res. 130:221–26 [Google Scholar]
  8. Beason-Held LL, Purpura KP, Van Meter JW, Azari NP, Mangot DJ. et al. 1998. PET reveals occipitotemporal pathway activation during elementary form perception in humans. Vis. Neurosci. 15:503–10 [Google Scholar]
  9. Bergen JR, Julesz B. 1983. Parallel versus serial processing in rapid pattern discrimination. Nature 303:696–98 [Google Scholar]
  10. Bergen JR, Wilson HR, Cowan JD. 1979. Further evidence for four mechanisms mediating vision at threshold: sensitivities to complex gratings and aperiodic stimuli. J. Opt. Soc. Am. 69:1580–87 [Google Scholar]
  11. Bhushan N, Rao AR, Lohse GL. 1997. The texture lexicon: understanding the categorization of visual texture terms and their relationship to texture images. Cogn. Sci. 21:219–46 [Google Scholar]
  12. Brodatz P. 1965. Textures: A Photographic Album for Artists and Designers New York: Dover
  13. Caelli T. 1985. Three processing characteristics of visual texture segmentation. Spat. Vis. 1:19–30 [Google Scholar]
  14. Caelli T, Julesz B. 1978. On perceptual analyzers underlying visual texture discrimination: part I. Biol. Cybern. 28:167–75 [Google Scholar]
  15. Caelli T, Julesz B, Gilbert E. 1978. On perceptual analyzers underlying visual texture discrimination: part II. Biol. Cybern. 29:201–14 [Google Scholar]
  16. Cavanagh P, Mather G. 1989. Motion: the long and short of it. Spat. Vis. 4:103–29 [Google Scholar]
  17. Champagnat F, Idier J, Goussard Y. 1998. Stationary Markov random fields on a finite rectangular lattice. IEEE Trans. Inf. Theory 44:2901–16 [Google Scholar]
  18. Chichilnisky EJ, Kalmar RS. 2002. Functional asymmetries in ON and OFF ganglion cells of primate retina. J. Neurosci. 22:2737–47 [Google Scholar]
  19. Chubb C, Econopouly J, Landy MS. 1994. Histogram contrast analysis and the visual segregation of IID textures. J. Opt. Soc. Am. A 11:2350–74 [Google Scholar]
  20. Chubb C, Landy M. 1991. Orthogonal distribution analysis: a new approach to the study of texture perception. Computational Models of Visual Processing MS Landy, JA Movshon 291–301 Cambridge, MA: MIT Press [Google Scholar]
  21. Chubb C, Landy MS, Econopouly J. 2004. A visual mechanism tuned to black. Vis. Res. 44:3223–32 [Google Scholar]
  22. Chubb C, Nam J-H. 2000. Variance of high contrast textures is sensed using negative half-wave rectification. Vis. Res. 40:1677–94 [Google Scholar]
  23. Chubb C, Nam J-H, Bindman DR, Sperling G. 2007. The three dimensions of human visual sensitivity to first-order contrast statistics. Vis. Res. 47:2237–48 [Google Scholar]
  24. Chubb C, Scofield I, Chiao C-C, Sperling G. 2012. A method for analyzing the dimensions of preattentive visual sensitivity. J. Math. Psychol. 56:427–43 [Google Scholar]
  25. Chubb C, Sperling G. 1988. Drift-balanced random stimuli: a general basis for studying non-Fourier motion perception. J. Opt. Soc. Am. A 5:1986–2007 [Google Scholar]
  26. Chubb C, Sperling G. 1989. Two motion perception mechanisms revealed through distance-driven reversal of apparent motion. PNAS 86:2985–89 [Google Scholar]
  27. De Bonet JS, Viola P. 1998. A non-parametric multi-scale statistical model for natural images. Advances in Neural Information Processing Systems 10 MI Jordan, MJ Kearns, SA Solla 773–79 Cambridge, MA: MIT Press [Google Scholar]
  28. Efros AA, Leung TK. 1999. Texture synthesis by non-parametric sampling. Proc. Int. Conf. Comp. Vis., Corfu, Greece, Sept. 20–251033 [Google Scholar]
  29. Freeman J, Ziemba CM, Heeger DJ, Simoncelli EP, Movshon JA. 2013. A functional and perceptual signature of the second visual area in primates. Nat. Neurosci. 16:974–81 [Google Scholar]
  30. Gilbert EN. 1980. Random colorings of lattice on squares in the plane. SIAM J. Algebraic Discr. Methods 1:152–59 [Google Scholar]
  31. Graham N. 1989. Visual Pattern Analyzers. Oxford, UK: Clarendon Press
  32. Graham N, Sutter A. 1998. Spatial summation in simple (Fourier) and complex (non-Fourier) texture channels. Vis. Res. 38:231–57 [Google Scholar]
  33. Graham N, Sutter A, Venkatesan C. 1993. Spatial-frequency- and orientation-selectivity of simple and complex channels in region segregation. Vis. Res. 33:1893–911 [Google Scholar]
  34. Gurnsey R, Fleet DJ. 2001. Texture space. Vis. Res. 41:745–57 [Google Scholar]
  35. Hansen T, Giesel M, Gegenfurtner KR. 2008. Chromatic discrimination of natural objects. J. Vis. 8:12 [Google Scholar]
  36. Hu Q, Victor JD. 2010. A set of high-order spatiotemporal stimuli that elicit motion and reverse-phi percepts. J. Vis. 10:39 [Google Scholar]
  37. Inverso M, Sun P, Chubb C, Wright CE, Sperling G. 2016. Evidence against global attention filters selective for absolute bar-orientation in human vision. Atten. Percept. Psychophys. 78:293–308 [Google Scholar]
  38. Joseph JS, Chun MM, Nakayama K. 1997. Attentional requirements in a ‘preattentive’ feature search task. Nature 387:805–7 [Google Scholar]
  39. Julesz B. 1962. Visual pattern discrimination. IRE Trans. Inf. Theory 8:84–92 [Google Scholar]
  40. Julesz B. 1981a. Textons, the elements of texture perception, and their interactions. Nature 290:91–97 [Google Scholar]
  41. Julesz B. 1981b. A theory of preattentive texture discrimination based on first-order statistics of texture. Biol. Cybern. 41:131–38 [Google Scholar]
  42. Julesz B, Bergen JR. 1983. Textons, the fundamental elements in preattentive vision and perception of textures. Bell Syst. Technical J. 62:1619–45 [Google Scholar]
  43. Julesz B, Gilbert EN, Shepp LA, Frisch HL. 1973. Inability of humans to discriminate between visual textures that agree in second-order statistics—revisited. Perception 2:391–405 [Google Scholar]
  44. Julesz B, Gilbert EN, Victor JD. 1978. Visual discrimination of textures with identical third-order statistics. Biol. Cybern. 31:137–40 [Google Scholar]
  45. Keeble DR, Kingdom FA, Moulden B, Morgan MJ. 1995. Detection of orientationally multimodal textures. Vis. Res. 35:1991–2005 [Google Scholar]
  46. Komban SJ, Alonso JM, Zaidi Q. 2011. Darks are processed faster than lights. J. Neurosci. 31:8654–58 [Google Scholar]
  47. Kremkow J, Jin J, Komban SJ, Wang Y, Lashgari R. et al. 2014. Neuronal nonlinearity explains greater visual spatial resolution for darks than lights. PNAS 111:3170–75 [Google Scholar]
  48. Lamme VAF, Van Dijk BW, Spekreijse H. 1992. Texture segregation is processed by primary visual cortex in man and monkey. Evidence from VEP experiments. Vis. Res. 32:797–807 [Google Scholar]
  49. Landy MS, Bergen JR. 1991. Texture segregation and orientation gradient. Vis. Res. 31:679–91 [Google Scholar]
  50. Landy MS, Oruc I. 2002. Properties of second-order spatial frequency channels. Vis. Res. 42:2311–29 [Google Scholar]
  51. Li A, Lennie P. 1997. Mechanisms underlying segmentation of colored textures. Vis. Res. 37:83–97 [Google Scholar]
  52. Li A, Zaidi Q. 2000. Perception of three-dimensional shape from texture is based on patterns of oriented energy. Vis. Res. 40:217–42 [Google Scholar]
  53. Li A, Zaidi Q. 2004. Three-dimensional shape from non-homogeneous textures: carved and stretched surfaces. J. Vis. 4:103 [Google Scholar]
  54. Logvinenko AD. 2003. Method of quadratic approximation: a new approach to identification of analysers and channels in human vision. J. Math. Psychol. 47:495–506 [Google Scholar]
  55. Malik J, Perona P. 1990. Preattentive texture discrimination with early vision mechanisms. J. Opt. Soc. Am. A 7:923–32 [Google Scholar]
  56. Marr D. 1982. Vision New York: W. H. Freeman & Co.
  57. Morrone MC, Burr DC. 1988. Feature detection in human vision: a phase-dependent energy model. Proc. R. Soc. Lond. B 235:221–45 [Google Scholar]
  58. Motoyoshi I, Nishida S, Sharan L, Adelson EH. 2007. Image statistics and the perception of surface qualities. Nature 447:206–9 [Google Scholar]
  59. Nam J-H, Chubb C. 2000. Texture luminance judgments are approximately veridical. Vis. Res. 40:1695–709 [Google Scholar]
  60. Nichols Z, Nirenberg S, Victor J. 2013. Interacting linear and nonlinear characteristics produce population coding asymmetries between ON and OFF cells in the retina. J. Neurosci. 33:14958–73 [Google Scholar]
  61. Nothdurft HC. 1990. Texture discrimination by cells in the cat lateral geniculate nucleus. Exp. Brain Res. 82:48–66 [Google Scholar]
  62. Nothdurft HC. 1994. Common properties of visual segmentation. Ciba Found. Symp. 184:245–59 [Google Scholar]
  63. Nothdurft HC. 2000. Salience from feature contrast: additivity across dimensions. Vis. Res. 40:1183–201 [Google Scholar]
  64. Nothdurft HC, Li CY. 1985. Texture discrimination: representation of orientation and luminance differences in cells of the cat striate cortex. Vis. Res. 25:99–113 [Google Scholar]
  65. Oppenheim AV, Lim JS. 1981. The importance of phase in signals. Proc. IEEE 69:529–41 [Google Scholar]
  66. Pickard DK. 1980. Unilateral Markov fields. Adv. Appl. Probab. 12:655–71 [Google Scholar]
  67. Poirson A, Wandell B, Varner D, Brainard D. 1990. Surface characterizations of color thresholds. J. Opt. Soc. Am. A 7:783–89 [Google Scholar]
  68. Portilla J, Simoncelli EP. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40:49–71 [Google Scholar]
  69. Purpura KP, Victor JD, Katz E. 1994. Striate cortex extracts higher-order spatial correlations from visual textures. PNAS 91:8482–86 [Google Scholar]
  70. Rao AR, Lohse GL. 1996. Towards a texture naming system: identifying relevant dimensions of texture. Vis. Res. 36:1649–69 [Google Scholar]
  71. Reichardt W. 1961. Autocorrelation, a Principle for the Evaluation of Sensory Information by the Central Nervous System Cambridge, MA: MIT Press
  72. Robson JG. 1980. Neural images: the physiological basis of spatial vision. Visual Coding and Adaptability CS Harris 177–214 Hillsdale, NJ: Erlbaum [Google Scholar]
  73. Rosenholtz R, Huang J, Raj A, Balas BJ, Ilie L. 2012. A summary statistic representation in peripheral vision explains visual search. J. Vis. 12:414 [Google Scholar]
  74. Schmid AM, Purpura KP, Victor JD. 2014. Responses to orientation discontinuities in V1 and V2: physiological dissociations and functional implications. J. Neurosci. 34:3559–78 [Google Scholar]
  75. Schmid AM, Victor JD. 2014. Possible functions of contextual modulations and receptive field nonlinearities: pop-out and texture segmentation. Vis. Res. 104:57–67 [Google Scholar]
  76. Silva AE, Chubb C. 2014. The 3-dimensional, 4-channel model of human visual sensitivity to grayscale scrambles. Vis. Res. 101:94–107 [Google Scholar]
  77. Song Y, Baker CL Jr. 2007. Neuronal response to texture- and contrast-defined boundaries in early visual cortex. Vis. Neurosci. 24:65–77 [Google Scholar]
  78. Sutter A, Sperling G, Chubb C. 1995. Measuring the spatial frequency selectivity of second-order texture mechanisms. Vis. Res. 35:915–24 [Google Scholar]
  79. te Pas SF, Koenderink JJ. 2004. Visual discrimination of spectral distributions. Perception 33:1483–97 [Google Scholar]
  80. Tkačik G, Prentice JS, Victor JD, Balasubramanian V. 2010. Local statistics in natural scenes predict the saliency of synthetic textures. PNAS 107:18149–54 [Google Scholar]
  81. Todd JT, Norman JF, Koenderink JJ, Kappers AM. 1997. Effects of texture, illumination, and surface reflectance on stereoscopic shape perception. Perception 26:807–22 [Google Scholar]
  82. Todd JT, Oomes AH, Koenderink JJ, Kappers AM. 2004. The perception of doubly curved surfaces from anisotropic textures. Psychol. Sci. 15:40–46 [Google Scholar]
  83. Treisman AM, Gelade G. 1980. A feature-integration theory of attention. Cogn. Psychol. 12:97–136 [Google Scholar]
  84. Victor JD. 1994. Images, statistics, and textures: implications of triple correlation uniqueness for texture statistics and the Julesz conjecture: comment. J. Opt. Soc. Am. A 11:1680–84 [Google Scholar]
  85. Victor JD, Brodie S. 1978. Discriminable textures with identical Buffon needle statistics. Biol. Cybern. 31:231–34 [Google Scholar]
  86. Victor JD, Conte MM. 1989. Cortical interactions in texture processing: scale and dynamics. Vis. Neurosci. 2:297–313 [Google Scholar]
  87. Victor JD, Conte MM. 1991. Spatial organization of nonlinear interactions in form perception. Vis. Res. 31:1457–88 [Google Scholar]
  88. Victor JD, Conte MM. 2004. Visual working memory for image statistics. Vis. Res. 44:541–56 [Google Scholar]
  89. Victor JD, Conte MM. 2006. Encoding and stability of image statistics in working memory. Vis. Res. 46:4152–62 [Google Scholar]
  90. Victor JD, Conte MM. 2012. Local image statistics: maximum-entropy constructions and perceptual salience. J. Opt. Soc. Am. A 29:1313–45 [Google Scholar]
  91. Victor JD, Rizvi SM, Conte MM. 2017. Two representations of a high-dimensional perceptual space. Vis. Res. 1371–23 [Google Scholar]
  92. Victor JD, Thengone DJ, Rizvi SM, Conte MM. 2015. A perceptual space of local image statistics. Vis. Res. 117:117–35 [Google Scholar]
  93. Wijntjes MWA, Pont SC. 2010. Illusory gloss on Lambertian surfaces. J. Vis. 10:913 [Google Scholar]
  94. Wolfson SS, Landy MS. 1995. Discrimination of orientation-defined texture edges. Vis. Res. 35:2863–77 [Google Scholar]
  95. Wolfson SS, Landy MS. 1999. Long range interactions between oriented texture elements. Vis. Res. 39:933–45 [Google Scholar]
  96. Xing D, Yeh C-I, Shapley RM. 2010. Generation of black-dominant responses in V1 cortex. J. Neurosci. 30:13504–12 [Google Scholar]
  97. Xu Y-Q, Guo B, Shum H. 2000. Chaos mosaic: fast and memory efficient texture synthesis Microsoft Tech. Rep. MSR-TR-2000-32
  98. Yellott J. 1993. Implications of triple correlation uniqueness for texture statistics and the Julesz conjecture. J. Opt. Soc. Am. A 5:777–93 [Google Scholar]
  99. Yu Y, Schmid AM, Victor JD. 2015. Visual processing of informative multipoint correlations arises primarily in V2. eLife 4:e06604 [Google Scholar]
  100. Zemon V, Gordon J, Welch J. 1988. Asymmetries in ON and OFF visual pathways of humans revealed using contrast-evoked cortical potentials. Vis. Neurosci. 1:145–50 [Google Scholar]
  101. Zhu SC, Wu Y, Mumford D. 1998. Filters, random fields and maximum entropy (FRAME): towards a unified theory for texture modeling. Int. J. Comput. Vis. 27:107–26 [Google Scholar]
/content/journals/10.1146/annurev-vision-102016-061316
Loading
Textures as Probes of Visual Processing
Annual Review of Vision Science 3, 275 (2017); https://doi.org/10.1146/annurev-vision-102016-061316
/content/journals/10.1146/annurev-vision-102016-061316
/content/journals/10.1146/annurev-vision-102016-061316
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error