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Investigating Color Illusions from the Perspective of Computational Color Constancy (2312.13114v1)

Published 20 Dec 2023 in cs.CV

Abstract: Color constancy and color illusion perception are two phenomena occurring in the human visual system, which can help us reveal unknown mechanisms of human perception. For decades computer vision scientists have developed numerous color constancy methods, which estimate the reflectance of the surface by discounting the illuminant. However, color illusions have not been analyzed in detail in the field of computational color constancy, which we find surprising since the relationship they share is significant and may let us design more robust systems. We argue that any model that can reproduce our sensation on color illusions should also be able to provide pixel-wise estimates of the light source. In other words, we suggest that the analysis of color illusions helps us to improve the performance of the existing global color constancy methods, and enable them to provide pixel-wise estimates for scenes illuminated by multiple light sources. In this study, we share the outcomes of our investigation in which we take several color constancy methods and modify them to reproduce the behavior of the human visual system on color illusions. Also, we show that parameters purely extracted from illusions are able to improve the performance of color constancy methods. A noteworthy outcome is that our strategy based on the investigation of color illusions outperforms the state-of-the-art methods that are specifically designed to transform global color constancy algorithms into multi-illuminant algorithms.

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References (68)
  1. Cross-camera convolutional color constancy. In Int. Conf. Comput. Vision, pages 1981–1990, Montreal, QC, Canada. IEEE/CVF.
  2. Sensor-independent illumination estimation for DNN models. In Brit. Mach. Vision Conf., Cardiff, UK. BMVA Press.
  3. Deep white-balance editing. In Conf. Comput. Vision Pattern Recognit., pages 1397–1406, Seattle, WA, USA. IEEE/CVF.
  4. Auto white-balance correction for mixed-illuminant scenes. In Winter Conf. Appl. Comput. Vision, pages 1210–1219, Waikoloa, HI, USA. IEEE/CVF.
  5. N-white balancing: White balancing for multiple illuminants including non-uniform illumination. IEEE Access, 10:89051–89062.
  6. Bach, M. (Last accessed: 18.02.2023). Color assimilation illusions. michaelbach.de/ot.
  7. Optical illusions. Adv. Clin. Neurosci. Rehabil., 6(2):20–21.
  8. Multi-illuminant estimation with conditional random fields. IEEE Trans. Image Process., 23:83–96.
  9. Single and multiple illuminant estimation using convolutional neural networks. IEEE Trans. Image Process., 26(9):4347–4362.
  10. Color constancy and non-uniform illumination: Can existing algorithms work? In IEEE Int. Conf. Comput. Vision Workshops, pages 774–781. IEEE.
  11. Bayesian model of human color constancy. J. Vision, 6(11):10–10.
  12. Color constancy. The Visual Neurosciences, 1:948–961.
  13. Buchsbaum, G. (1980). A spatial processor model for object colour perception. J. Franklin Inst., 310:1–26.
  14. Analysis of biases in automatic white balance datasets and methods. Color Res. Appl., 48(1):40–62.
  15. Illuminant estimation for color constancy: Why spatial-domain methods work and the role of the color distribution. J. Opt. Soc. America A, 31:1049–1058.
  16. What are lightness illusions and why do we see them? PLoS Comput. Biol., 3(9):e180.
  17. Generative models for multi-illumination color constancy. In Conf. Comput. Vision Pattern Recognit., pages 1194–1203, Montreal, BC, Canada. IEEE/CVF.
  18. Spatial filtering, color constancy, and the color-changing dress. J. Vision, 17(3):7–7.
  19. One-net: Convolutional color constancy simplified. Pattern Recognit. Letters, 159:31–37.
  20. Specularity, the zeta-image, and information-theoretic illuminant estimation. In Workshops Demonstrations: Eur. Conf. Comput. Vision, pages 411–420, Florence, Italy. Springer.
  21. Ebner, M. (2003). Combining white-patch retinex and the gray world assumption to achieve color constancy for multiple illuminants. In Joint Pattern Recognit. Symp., pages 60–67, Magdeburg, Germany. Springer.
  22. Ebner, M. (2004). A parallel algorithm for color constancy. J. Parallel Distrib. Comput., 64:79–88.
  23. Ebner, M. (2007). Color Constancy, 1st ed. Wiley Publishing, ISBN: 0470058299.
  24. Ebner, M. (2009). Color constancy based on local space average color. Mach. Vision Appl., 20(5):283–301.
  25. Ebner, M. (2011). On the effect of scene motion on color constancy. Biol. Cybern., 105(5):319–330.
  26. Individual differences and their implications for color perception. Current Opinion Behavioral Sciences, 30:28–33.
  27. Physically-plausible illumination distribution estimation. In Int. Conf. Comput. Vision, pages 12928–12936. IEEE/CVF.
  28. Color constancy at a pixel. J. Opt. Soc. America A, 18(2):253–264.
  29. Shades of gray and colour constancy. In Color and Imag. Conf., pages 37–41, Scottsdale, AZ, USA. Society for Imaging Science and Technology.
  30. Retinex in matlab™. J. Electron. Imag., 13(1).
  31. Efficient color constancy with local surface reflectance statistics. In Eur. Conf. Comput. Vision, pages 158–173, Zurich, Switzerland. Springer.
  32. Improving color constancy by discounting the variation of camera spectral sensitivity. J. Opt. Soc. America A, 34:1448–1462.
  33. Combining bottom-up and top-down visual mechanisms for color constancy under varying illumination. IEEE Trans. Image Process., 28(9):4387–4400.
  34. Color constancy using double-opponency. IEEE Transactions Pattern Anal. Mach. Intell., 37(10):1973–1985.
  35. Gegenfurtner, K. R. (1999). Reflections on colour constancy. Nature, 402(6764):855–856.
  36. Bayesian color constancy revisited. In Conf. Comput. Vision Pattern Recognit., pages 1–8, Anchorage, AK, USA. IEEE.
  37. Physics-based edge evaluation for improved color constancy. In Conf. Comput. Vision Pattern Recognit., pages 581–588, Miami, FL, USA. IEEE.
  38. Color constancy for multiple light sources. IEEE Trans. Image Process., 21(2):697–707.
  39. Convolutional neural networks can be deceived by visual illusions. In Conf. Comput. Vision Pattern Recognit., pages 12309–12317, Long Beach, CA, USA. IEEE/CVF.
  40. Rehabilitating the colorchecker dataset for illuminant estimation. In Color Imag. Conf., pages 350–353, Vancouver, BC, Canada. Society for Imaging Science and Technology.
  41. Hurlbert, A. (2007). Colour constancy. Current Biology, 17(21):R906–R907.
  42. Color constancy algorithm for mixed-illuminant scene images. IEEE Access, 6:8964–8976.
  43. Color constancy for uniform and non-uniform illuminant using image texture. IEEE Access, 7:72964–72978.
  44. The role of bright pixels in illumination estimation. In Color Imag. Conf., pages 41–46, Los Angeles, CA, USA. Society for Imaging Science and Technology.
  45. Kitaoka, A. (Last accessed: 18.02.2023). Color illusions. ritsumei.ac.jp/ akitaoka/index-e.html.
  46. Blur and disorder. J. Visual Communication Image Representation, 11(2):237–244.
  47. Color constancy convolutional autoencoder. In Symp. Ser. Comput. Intell., pages 1085–1090, Xiamen, China. IEEE.
  48. Intel-tau: A color constancy dataset. IEEE Access, 9:39560–39567.
  49. Land, E. H. (1977). The retinex theory of color vision. Scientific Amer., 237:108–129.
  50. Lightness and retinex theory. J. Opt. Soc. America A, 61(1):1–11.
  51. Space-average scene colour used to extract illuminant information. John Dalton’s Colour Vision Legacy, pages 501–509.
  52. A computational approach to color illusions. In Int. Conf. Image Anal. Process, pages 62–69, Florence, Italy. Springer.
  53. A computational approach to color adaptation effects. Image Vision Comput., 18(13):1005–1014.
  54. Human color constancy based on the geometry of color distributions. J. Vision, 21(3):7–7.
  55. Degree-of-linear-polarization-based color constancy. In Conf. Comput. Vision Pattern Recognit., pages 19740–19749, New Orleans, LA, USA. IEEE/CVF.
  56. On finding gray pixels. In Conf. Comput. Vision Pattern Recognit., pages 8062–8070, Long Beach, CA, USA. IEEE/CVF.
  57. Revisiting gray pixel for statistical illumination estimation. arXiv preprint arXiv:1803.08326.
  58. Two visual pathways in primates based on sampling of space: exploitation and exploration of visual information. Frontiers Integrative Neuroscience, 10:37.
  59. Estimating illuminant color based on luminance balance of surfaces. J. Opt. Soc. America A, 29(2):A133–A143.
  60. BIO-CC: Biologically inspired color constancy. In Brit. Mach. Vision Conf., London, UK. BMVA Press.
  61. Color constancy beyond standard illuminants. In Int. Conf. Image Process., pages 2826–2830, Bordeaux, France. IEEE.
  62. Block-based color constancy: The deviation of salient pixels. In Int. Conf. Acoust. Speech Signal Process., pages 1–5, Rhodes Island, Greece. IEEE.
  63. Multi-scale color constancy based on salient varying local spatial statistics. Vis. Comput., pages 1–17.
  64. Edge-based color constancy. IEEE Trans. Image Process., 16:2207–2214.
  65. Color constancy via multi-scale region-weighed network guided by semantics. Frontiers Neurorobotics, 16:841426.
  66. Efficient illuminant estimation for color constancy using grey pixels. In Conf. Comput. Vision Pattern Recognit., pages 2254–2263, Boston, USA. IEEE/CVF.
  67. Zeki, S. (1993). A Vision of the Brain. Blackwell Science, ISBN: 0632030545.
  68. A retinal mechanism inspired color constancy model. IEEE Trans. Image Process., 25(3):1219–1232.
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Authors (3)
  1. Oguzhan Ulucan (3 papers)
  2. Diclehan Ulucan (1 paper)
  3. Marc Ebner (1 paper)
Citations (1)

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