How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?

PLoS Computational Biology

Volumn 9, Issue 1, 2013, Pages

  Gerhard, Holly E ^a,b,c   Wichmann, Felix A ^b,c,d   Bethge, Matthias ^a,b,c  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b UNIVERSITY OF TÜBINGEN   (Germany)

^c Bernstein Center for Computational Neuroscience   (Germany)

^d MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

PHYSIOLOGICAL MODELS;

HUMAN VISUAL SYSTEM; IMAGE MODELING; LOCAL STATISTICS; NATURAL IMAGES; NEURAL RESPONSE; PROBABILISTIC MODELS; PSYCHOPHYSICAL STUDIES; SENSORY SIGNALS; SENSORY SYSTEM; STATISTICAL REGULARITY;

INFORMATION THEORY;

ARTICLE; DISCRIMINANT ANALYSIS; HUMAN; IMAGE ANALYSIS; LUMINANCE; NEUROSCIENCE; PROBABILITY; SENSORY SYSTEM; SPECTROSCOPY; VISUAL SYSTEM;

EID: 84873514336     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002873     Document Type: Article

References (62)

1. Attneave F, (1954) Some informational aspects of visual perception. Psychological review 61: 183-193.
2. Barlow H, (1959) Sensory mechanisms, the reduction of redundancy, and intelligence. In: The mechanisation of thought processes. Her Majesty's Stationery Office London. pp. 535-539.
3. Atick JJ, Redlich AN, (1992) What does the retina know about natural scenes? Neural Computation 4: 196-210.
4. Dan Y, Atick JJ, Reid RC, (1996) Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. The Journal of Neuroscience 16: 3351-3362.
5. Olshausen BA, Field DJ, (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607-609.
6. Bell AJ, Sejnowski TJ, (1997) The "Independent Components" of Natural Scenes are Edge Filters. Vision Research 37: 3327-3338.
7. Schwartz O, Simoncelli EP, (2001) Natural signal statistics and sensory gain control. Nature Neuroscience 4: 819-825.
8. Eichhorn J, Sinz F, Bethge M, (2009) Natural image coding in V1: how much use is orientation selectivity? PLoS Computational Biology 5: 1-16.
9. Lyu S, Simoncelli EP, (2009) Nonlinear extraction of independent components of natural images using radial gaussianization. Neural Computation 21: 1485-1519.
10. Sinz F, Bethge M, (2008) The conjoint effect of divisive normalization and orientation selectivity on redundancy reduction. In: Advances in Neural Information Processing Systems 21: 1521-1528.
11. Karklin Y, Lewicki MS, (2009) Emergence of complex cell properties by learning to generalize in natural scenes. Nature 457: 83-86.
12. Deriugin N, (1956) The power spectrum and the correlation function of the television signal. Telecommunications 1: 1-12.
13. Knill DC, Field D, Kersten D, (1990) Human discrimination of fractal images. Journal of the Optical Society of America A 7: 1113-1123.
14. Tadmor Y, Tolhurst DJ, (1994) Discrimination of changes in the second-order statistics of natural and synthetic images. Vision Research 34: 541-554.
15. Tolhurst DJ, Tadmor Y, (2000) Discrimination of spectrally blended natural images: Optimisation of the human visual system for encoding natural images. Perception 29: 1087-1100.
16. Párraga C, Troscianko T, Tolhurst DJ, (2000) The human visual system is optimised for processing the spatial information in natural visual images. Current Biology 10: 35-38.
17. Párraga C, Troscianko T, Tolhurst DJ, (2005) The effects of amplitude-spectrum statistics on foveal and peripheral discrimination of changes in natural images, and a multi-resolution model. Vision Research 45: 3145-3168.
18. Tadmor Y, Tolhurst DJ, (1993) Both the phase and the amplitude spectrum may determine the appearance of natural images. Vision Research 33: 141-145.
19. Thomson MGA, Foster DH, (1997) Role of second- and third-order statistics in the discriminability of natural images. Journal of the Optical Society of America A 14: 2081-2090.
20. Thomson MGA, Foster DH, Summers RJ, (2000) Human sensitivity to phase perturbations in natural images: a statistical framework. Perception 29: 1057-1069.
21. Wichmann FA, Braun DI, Gegenfurtner KR, (2006) Phase noise and the classification of natural images. Vision Research 46: 1520-1529.
22. Hansen BC, Hess RF, (2007) Structural sparseness and spatial phase alignment in natural scenes. Journal of the Optical Society of America A 24: 1873-1885.
23. Baker C, Yoonessi A, Arsenault E, (2008) Texture segmentation in natural images: Contribution of higher-order image statistics to psychophysical performance. Journal of Vision 8: 350.
24. Joubert OR, Rousselet GA, Fabre-Thorpe M, Fize D, (2009) Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise. Journal of Vision 9: 2.1-16.
25. Emrith K, Chantler MJ, Green PR, Maloney LT, Clarke ADF, (2010) Measuring perceived differences in surface texture due to changes in higher order statistics. Journal of the Optical Society of America A 27: 1232-1244.
26. Oppenheim AV, Lim JS, (1981) Importance of Phase in Signals. In: Proceedings of the IEEE volume 69: 529-541.
27. Kersten D, (1987) Predictability and redundancy of natural images. Journal of the Optical Society of America A 4: 2395-2400.
28. Heeger DJ, Bergen JR (1995) Pyramid-based texture analysis/synthesis. In: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques. SIGGRAPH. New York, NY, USA: ACM. pp. 229-238.
29. Portilla J, Simoncelli EP, (2000) A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 40: 49-70.
30. Balas BJ, (2006) Texture synthesis and perception: Using computational models to study texture representations in the human visual system. Vision Research 46: 299-309.
31. Wainwright M, Simoncelli E, (2000) Scale mixtures of gaussians and the statistics of natural images. In: Advances in Neural Information Processing Systems 12: 855-861.
32. Guerrero-Colon J, Simoncelli E, Portilla J, (2008) Image denoising using mixtures of gaussian scale mixtures. In: ICIP 15th IEEE International Conference on Image Processing pp. 565-568.
33. Bethge M, Hosseini R (2008). Method and device for image compression. Available: https://register.epo.org/espacenet/application?number=EP08010343.
34. Julesz B, (1962) Visual pattern discrimination. IRE Transactions on Information Theory 8: 84-92.
35. Wolpert DH, Macready WG, (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1: 67-82.
36. Chubb C, Landy MS, Econopouly J, (2004) A visual mechanism tuned to black. Vision Research 44: 3223-3232.
37. Julesz B, Gilbert EN, Victor JD, (1978) Visual discrimination of textures with identical third-order statistics. Biological Cybernetics 31: 137-140.
38. Julesz B, (1981) Textons, the elements of texture perception, and their interactions. Nature 290: 91-97.
39. Victor JD, Conte MM, (1991) Spatial organization of nonlinear interactions in form perception. Vision Research 31: 1457-1488.
40. Victor JD, Chubb C, Conte MM, (2005) Interaction of luminance and higher-order statistics in texture discrimination. Vision Research 45: 311-328.
41. van Hateren JH, van der Schaaf A, (1998) Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society B 265: 359-366.
42. Hyvärinen A, Hurri J, Hoyer P, (2009) Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. Springer.
43. Bethge M, (2006) Factorial coding of natural images: How effective are linear models in removing higher-order dependencies? Journal of the Optical Society of America A 23: 1253-1268.
44. Morgan MJ, Hayes A, (1991) The relative importance of local phase and local amplitude in patchwise image reconstruction. Biological Cybemetics 119: 113-119.
45. Fründ I, Haenel NV, Wichmann FA, (2011) Inference for psychometric functions in the presence of nonstationary behavior. Journal of Vision 11 piii: 16.
46. Klein SA, Tyler CW, (1986) Phase discrimination of compound gratings: generalized autocorrelation analysis. Journal of the Optical Society of America A 3: 868-879.
47. Tyler CW, (2004) Beyond fourth-order texture discrimination: generation of extreme-order and statistically-balanced textures. Vision Research 44: 2187-99.
48. Tyler CW, (2004) Theory of texture discrimination of based on higher-order perturbations in individual texture samples. Vision Research 44: 2179-86.
49. Victor JD, Conte MM, (2012) Local image statistics: maximum-entropy constructions and perceptual salience. Journal of the Optical Society of America A 29: 1313-1345.
50. Tkacik G, Prentice JS, Victor JD, Balasubramanian V, (2010) Local statistics in natural scenes predict the saliency of synthetic textures. Proceedings of the National Academy of Sciences of the United States of America 107: 18149-54.
51. Bethge M, Wiecki TV, Wichmann FA, (2007) The independent components of natural images are perceptually dependent. In: Proceedings of SPIE Human Vision and Electronic Imaging XII (EI105) 6492: 1-12.
52. Elder JH, (1999) Are edges incomplete? International Journal of Computer Vision 34: 97-122.
53. Girshick AR, Landy MS, Simoncelli EP, (2011) Cardinal rules: visual orientation perception reects knowledge of environmental statistics. Nature Neuroscience 14: 926-32.
54. Balas B, Nakano L, Rosenholtz R, (2009) A summary-statistic representation in peripheral vision explains visual crowding. Journal of Vision 9: 13.
55. Rosenholtz R, Huang J, Ehinger KA, (2012) Rethinking the role of top-down attention in vision: effects attributable to a lossy representation in peripheral vision. Frontiers in Psychology 3: 13.
56. Freeman J, Simoncelli EP, (2011) Metamers of the ventral stream. Nature Neuroscience 14: 1195-1201.
57. Brainard DH, (1997) The psychophysics toolbox. Spatial Vision 10: 433-436.
58. Kleiner M, Brainard D, Pelli D, (2007) What's new in Psychtoolbox-3? Perception 36: ECVP Abstract Supplement.
59. Sinz F, Hosseini R (2009). Natural image statistics density estimation toolbox (nisdet) for matlab. Available: http://bethgelab.org/software/nisdet/.
60. Hyvarinen A, (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10: 626-634.
61. Jaekel F, Wichmann FA, (2006) Spatial four-alternative forced-choice method is the preferred psychophysical method for naive observers. Journal of Vision 6: 1307-22.
62. Sinz F, Bethge M, (2010) lp-nested symmetric distributions. Journal of Machine Learning Research 11: 3409-3451.