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Independence of color and luminance edges in natural scenes

Published online by Cambridge University Press:  01 January 2009

THORSTEN HANSEN  and
KARL R. GEGENFURTNER
THORSTEN HANSEN*
Affiliation:
Abteilung Allgemeine Psychologie, Justus-Liebig-Universität Giessen, Giessen, Germany
KARL R. GEGENFURTNER
Affiliation:
Abteilung Allgemeine Psychologie, Justus-Liebig-Universität Giessen, Giessen, Germany
*
*Address correspondence and reprint requests to: Thorsten Hansen, Abteilung Allgemeine Psychologie, Justus-Liebig-Universität Giessen, Otto-Behaghel-Strasse 10F, 35394 Giessen, Germany. E-mail: [email protected]
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Abstract

Form vision is traditionally regarded as processing primarily achromatic information. Previous investigations into the statistics of color and luminance in natural scenes have claimed that luminance and chromatic edges are not independent of each other and that any chromatic edge most likely occurs together with a luminance edge of similar strength. Here we computed the joint statistics of luminance and chromatic edges in over 700 calibrated color images from natural scenes. We found that isoluminant edges exist in natural scenes and were not rarer than pure luminance edges. Most edges combined luminance and chromatic information but to varying degrees such that luminance and chromatic edges were statistically independent of each other. Independence increased along successive stages of visual processing from cones via postreceptoral color-opponent channels to edges. The results show that chromatic edge contrast is an independent source of information that can be linearly combined with other cues for the proper segmentation of objects in natural and artificial vision systems. Color vision may have evolved in response to the natural scene statistics to gain access to this independent information.

Keywords

Color visionForm visionEdge detectionNatural scene statisticsMutual information
Type
Natural Scene Statistics and Efficient Coding
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 35 - 49
Copyright
Copyright © Cambridge University Press 2009

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References

Allen, G. (1879). The Colour-Sense: Its Origin and Development. London: Trubner & Co.Google Scholar
Barlow, H.B. (1961). Possible principles underlying the transformation of sensory messages. In Sensory Communication, ed. Rosenblith, W.A., pp. 217–234. Cambridge, MA: MIT Press.Google Scholar
Bradley, A., Switkes, E. & Valois, K.D. (1988). Orientation and spatial frequency selectivity of adaptation to color and luminance gratings. Vision Research 28, 841–856.CrossRefGoogle ScholarPubMed
Buchsbaum, G. & Gottschalk, A. (1983). Trichromacy, opponent colours coding and optimum colour information transmission in the retina. Proceedings of the Royal Society of London (Series B, Biological Science) 220(1218), 89–113.Google ScholarPubMed
Chatterjee, S. & Callaway, E.M. (2003). Parallel colour-opponent pathways to primary visual cortex. Nature, 426(6967), 668–671.CrossRefGoogle ScholarPubMed
Chechik, G., Globerson, A., Tishby, N., Anderson, M.J., Young, E.D. & Nelken, I. (2002). Group redundancy measures reveal redundancy reduction in the auditory pathway. In Advances in Neural Information Processing Systems 14, ed. Dietterich, T.G., Becker, S. & Ghahramani, Z., pp. 173–180. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Clifford, C.W., Spehar, B., Solomon, S.G., Martin, P.R. & Zaidi, Q. (2003). Interactions between color and luminance in the perception of orientation. Journal of Vision 3, 106–115.CrossRefGoogle ScholarPubMed
Conway, B.R. (2001). Spatial structure of cone inputs to color cells in alert macaque primary visual cortex (V-1). Journal of Neuroscience 21, 2768–2783.CrossRefGoogle ScholarPubMed
Conway, B.R., Hubel, D.H. & Livingstone, M.S. (2002). Color contrast in macaque V1. Cerebral Cortex 12, 915–925.CrossRefGoogle ScholarPubMed
Conway, B.R. & Livingstone, M.S. (2006). Spatial and temporal properties of cone signals in alert macaque primary visual cortex. Journal of Neuroscience 26, 10826–10846.CrossRefGoogle ScholarPubMed
Dacey, D.M. (2000). Parallel pathways for spectral coding in primate retina. Annual Reviews in Neuroscience 23, 743–775.CrossRefGoogle ScholarPubMed
De Valois, R.L. & De Valois, K.K. (1988). Spatial Vision, Vol. 14 of Oxford Psychology Series. New York: Oxford University Press.Google Scholar
Deco, G. & Obradovic, D. (1997). An Information-Theoretic Approach to Neural Computing. Berlin, Germany: Springer.Google Scholar
Delcroix, C.J. & Abidi, M.A. (1988). Fusion of edge maps in color images. In SPIE Proceedings, Visual Communications and Image Processing, ed. Hsing, T.R., SPIE - The International Society for Optical Engineering, Cambridge, Massachusetts, USA. Vol. 1001, pp. 545–554.Google Scholar
Fine, I., MacLeod, D.I. & Boynton, G.M. (2003). Surface segmentation based on the luminance and color statistics of natural scenes. Journal of the Optical Society of America (A) 20, 1283–1291.CrossRefGoogle ScholarPubMed
Forsyth, D. & Ponce, J. (2002). Computer Vision—A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Friedman, H.S., Zhou, H. & von der Heydt, R. (2003). The coding of uniform colour figures in monkey visual cortex. Journal of Physiology 548, 593–613.CrossRefGoogle ScholarPubMed
Gegenfurtner, K.R., Kiper, D.C. & Fenstemaker, S.B. (1996). Processing of color, form, and motion in macaque area V2. Visual Neuroscience 13, 161–172.CrossRefGoogle ScholarPubMed
Gegenfurtner, K.R. & Rieger, J. (2000). Sensory and cognitive contributions of color to the recognition of natural scenes. Current Biology 10, 805–808.CrossRefGoogle Scholar
Geisler, W.S. (2008). Visual perception and the statistical properties of natural scenes. Annual Reviews in Psychology 59, 167–192.CrossRefGoogle ScholarPubMed
Gevers, T. & Smeulders, A.M. (2000). Color based object recognition. Pattern Recognition 32, 453–464.CrossRefGoogle Scholar
Gheorghiu, E. & Kingdom, F.A. (2007). Chromatic tuning of contour-shape mechanisms revealed through the shape-frequency and shape-amplitude after-effects. Vision Research 47, 1935–1949.CrossRefGoogle ScholarPubMed
Grossberg, S. (1987). Cortical dynamics of three-dimensional form, color, and brightness perception: I. Monocular theory. Perception & Psychophysics 41, 87–116.CrossRefGoogle ScholarPubMed
Grossberg, S. (2000). The complementary brain: Unifying brain dynamics and modularity. Trends in Cognitive Sciences 4, 233–246.CrossRefGoogle ScholarPubMed
Hamburger, K., Hansen, T. & Gegenfurtner, K.R. (2007). Geometric-optical illusions at isoluminance. Vision Research 47, 3276–3285.CrossRefGoogle ScholarPubMed
Hansen, T. & Gegenfurtner, K.R. (2007). Chromatic and luminance edges in natural scenes. Perception 36(Suppl.), 193. 30th European Conference on Visual Perception (ECVP 2007), Arezzo, Italy.Google Scholar
Heidemann, G. (2006). The principal components of natural images revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 822–826.CrossRefGoogle ScholarPubMed
Hubel, D.H. & Wiesel, T.N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology 195, 215–243.CrossRefGoogle ScholarPubMed
Jacobs, R.A. (1995). Methods for combining experts’ probability assessments. Neural Computation 7, 867–888.CrossRefGoogle ScholarPubMed
Johnson, A.P., Kingdom, F.A. & Baker, C.L Jr.. (2005). Spatiochromatic statistics of natural scenes: First- and second-order information and their correlational structure. Journal of the Optical Society of America (A) 22, 2050–2059.CrossRefGoogle ScholarPubMed
Johnson, E.N., Hawken, M.J. & Shapley, R. (2001). The spatial transformation of color in the primary visual cortex of the macaque monkey. Nature Neuroscience 4, 409–416.CrossRefGoogle ScholarPubMed
Johnson, E.N., Hawken, M.J. & Shapley, R. (2004). Cone inputs in macaque primary visual cortex. Journal of Neurophysiology 91, 2501–2514.CrossRefGoogle ScholarPubMed
Johnson, E.N., Hawken, M.J. & Shapley, R. (2008). The orientation selectivity of color-responsive neurons in macaque V1. Journal of Neuroscience 28, 8096–8106.CrossRefGoogle ScholarPubMed
Kelly, D.H. (1983). Spatiotemporal variation of chromatic and achromatic contrast thresholds. Journal of the Optical Society of America 73, 742–750.CrossRefGoogle ScholarPubMed
Kingdom, F.A. (2003). Color brings relief to human vision. Nature Neuroscience 6, 641–644.CrossRefGoogle ScholarPubMed
Kingdom, F.A., Beauce, C. & Hunter, L. (2004). Colour vision brings clarity to shadows. Perception 33, 907–914.CrossRefGoogle ScholarPubMed
Klinker, G.J. & Shafer, S.A. (1990). A physical approach to color image understanding. International Journal of Computer Vision 4, 7–38.CrossRefGoogle Scholar
Lee, D. (1990). Coping with discontinuities in computer vision: Their detection, classification, and measurement. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 321–344.CrossRefGoogle Scholar
Lennie, P. (1999). Color coding in the cortex. In Color Vision—From Genes to Perception, ed. Gegenfurtner, K.R. & Sharpe, L.T., pp. 235–247. New York: Cambridge University Press.Google Scholar
Lindeberg, T. (1994). Scale-Space Theory in Computer Vision. Boston, MA: Kluwer.CrossRefGoogle Scholar
Livingstone, M. & Hubel, D. (1988). Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science 240, 740–749.CrossRefGoogle ScholarPubMed
Livingstone, M.S. & Hubel, D.H. (1984). Anatomy and physiology of a color system in the primate visual cortex. Journal of Neuroscience 4, 309–356.CrossRefGoogle ScholarPubMed
Losada, M.A. & Mullen, K.T. (1995). Color and luminance spatial tuning estimated by noise masking in the absence of off-frequency looking. Journal of the Optical Society of America (A) 12, 250–260.CrossRefGoogle ScholarPubMed
Mante, V., Frazor, R.A., Bonin, V., Geisler, W.S. & Carandini, M. (2005). Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience 8, 1690–1697.CrossRefGoogle ScholarPubMed
Marr, D. (1982). Vision. San Francisco, CA: W.H. Freeman & Co.Google Scholar
Martin, D.R., Fowlkes, C.C. & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 530–549.CrossRefGoogle ScholarPubMed
McIlhagga, W.H. & Mullen, K.T. (1996). Contour integration with colour and luminance contrast. Vision Research 36, 1265–1279.CrossRefGoogle ScholarPubMed
Mullen, K.T. (1985). The contrast sensitivity of human colour vision to red-green and blue-yellow chromatic gratings. Journal of Physiology 359, 381–400.CrossRefGoogle ScholarPubMed
Mullen, K.T. & Beaudot, W.H. (2002). Comparison of color and luminance vision on a global shape discrimination task. Vision Research 42, 565–575.CrossRefGoogle ScholarPubMed
Mullen, K.T., Beaudot, W.H. & McIlhagga, W.H. (2000). Contour integration in color vision: A common process for the blue-yellow, red-green and luminance mechanisms? Vision Research 40, 639–655.CrossRefGoogle ScholarPubMed
Neumann, H., Pessoa, L. & Hansen, T. (2001). Visual filling-in for computing perceptual surface properties. Biological Cybernetics 85, 355–369.CrossRefGoogle ScholarPubMed
Nevatia, R. (1977). A color edge detector and its use in scene segmentation. IEEE Transactions on Systems, Man, and Cybernetics 7, 820–826.Google Scholar
Olmos, A. & Kingdom, F.A. (2004 a). McGill calibrated colour image database. http://tabby.vision.mcgill.ca.Google Scholar
Olmos, A. & Kingdom, F.A. (2004 b). McGill calibrated colour image database: Details of calibration. http://tabby.vision.mcgill.ca/html/Extras/McGillCameraCalibration.pdf.Google Scholar
Párraga, C.A. (1995). Spatiochromatic Information Content of Natural Scenes. Master’s thesis, Bristol, England: University of Bristol.Google Scholar
Párraga, C.A., Brelstaff, G., Troscianko, T. & Moorehead, I.R. (1998). Color and luminance information in natural scenes. Journal of the Optical Society of America (A) 15, 563–569.CrossRefGoogle ScholarPubMed
Párraga, C.A., Troscianko, T. & Tolhurst, D.J. (2002). Spatiochromatic properties of natural images and human vision. Current Biology 12, 483–487.CrossRefGoogle ScholarPubMed
Poirson, A.B. & Wandell, B.A. (1993). Appearance of colored patterns: Pattern-color separability. Journal of the Optical Society of America (A) 10, 2458–2470.CrossRefGoogle ScholarPubMed
Poirson, A.B. & Wandell, B.A. (1996). Pattern-color separable pathways predict sensitivity to simple colored patterns. Vision Research 36, 515–526.CrossRefGoogle ScholarPubMed
Polyak, S. (1957). The Vertebrate Visual System. Chicago, IL: Chicago University Press.Google Scholar
Reisbeck, T.E. & Gegenfurtner, K.R. (1998). Effects of contrast and temporal frequency on orientation discrimination for luminance and isoluminant stimuli. Vision Research 38, 1105–1117.CrossRefGoogle ScholarPubMed
Ruderman, D.L., Cronin, T.W. & Chiao, C.C. (1998). Statistics of cone responses to natural images: Implications for visual coding. Journal of the Optical Society of America (A) 15, 2036–2045.CrossRefGoogle Scholar
Schwartz, O. & Simoncelli, E.P. (2001). Natural signal statistics and sensory gain control. Nature Neuroscience 4, 819–825.CrossRefGoogle ScholarPubMed
Sekiguchi, N., Williams, D.R. & Brainard, D.H. (1993). Aberration-free measurements of the visibility of isoluminant gratings. Journal of the Optical Society of America (A) 10, 2105–2117.CrossRefGoogle ScholarPubMed
Shafer, S.A. (1985). Using color to separate reflection components. Color Research & Application 10, 210–218.CrossRefGoogle Scholar
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656.CrossRefGoogle Scholar
Shevell, S.K. & Kingdom, F.A. (2008). Color in complex scenes. Annual Reviews in Psychology 59, 143–166.CrossRefGoogle ScholarPubMed
Simoncelli, E.P. (2003). Vision and the statistics of the visual environment. Current Opinion in Neurobiology 13, 144–149.CrossRefGoogle ScholarPubMed
Simoncelli, E.P. & Olshausen, B.A. (2001). Natural image statistics and neural representation. Annual Reviews in Neuroscience 24, 1193–1216.CrossRefGoogle ScholarPubMed
Smith, V.C. & Pokorny, J. (1975). Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm. Vision Research 15, 161–171.CrossRefGoogle ScholarPubMed
Stockman, A. & Sharpe, L.T. (2000). The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype. Vision Research 40, 1711–1737.CrossRefGoogle ScholarPubMed
Sumner, P., Anderson, E.J., Sylvester, R., Haynes, J.D. & Rees, G. (2008). Combined orientation and colour information in human V1 for both L-M and S-cone chromatic axes. Neuroimage 39, 814–824.CrossRefGoogle Scholar
Sumner, P. & Mollon, J.D. (2000 a). Catarrhine photopigments are optimized for detecting targets against a foliage background. Journal of Experimental Biology 203, 1963–1986.CrossRefGoogle ScholarPubMed
Sumner, P. & Mollon, J.D. (2000 b). Chromaticity as a signal of ripeness in fruits taken by primates. Journal of Experimental Biology 203, 1987–2000.CrossRefGoogle ScholarPubMed
Switkes, E., Bradley, A. & Valois, K.K.D. (1988). Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings. Journal of the Optical Society of America (A) 5, 1149–1162.CrossRefGoogle ScholarPubMed
Thorell, L.G., De Valois, R.L. & Albrecht, D.G. (1984). Spatial mapping of monkey V1 cells with pure color and luminance stimuli. Vision Research 24, 751–769.CrossRefGoogle ScholarPubMed
van de Weijer, J., Gevers, T. & Smeulders, A.W. (2006). Robust photometric invariant features from the color tensor. IEEE Transactions on Image Processing 15, 118–127.CrossRefGoogle ScholarPubMed
van Hateren, J.H. (1993). Spatial, temporal and spectral pre-processing for colour vision. Proceedings of the Royal Society of London (Series B, Biological Science) 251(1330), 61–68.Google ScholarPubMed
Wachtler, T., Lee, T.W. & Sejnowski, T.J. (2001). Chromatic structure of natural scenes. Journal of the Optical Society of America (A) 18, 65–77.CrossRefGoogle ScholarPubMed
Webster, M.A., De Valois, K.K. & Switkes, E. (1990). Orientation and spatial-frequency discrimination for luminance and chromatic gratings. Journal of the Optical Society of America (A) 7, 1034–1049.CrossRefGoogle ScholarPubMed
Webster, M.A. & Mollon, J.D. (1997). Adaptation and the color statistics of natural images. Vision Research 37, 3283–3298.CrossRefGoogle ScholarPubMed
Wichmann, F.A., Sharpe, L.T. & Gegenfurtner, K.R. (2002). The contributions of color to recognition memory for natural scenes. Journal of Experimental Psychology: Learning, Memory, and Cognition 28, 509–520.Google ScholarPubMed
Zetzsche, C. & Röhrbein, F. (2001). Nonlinear and extra-classical receptive field properties and the statistics of natural scenes. Network 12, 331–350.CrossRefGoogle ScholarPubMed
Zhou, C. & Mel, B.W. (2008). Cue combination and color edge detection in natural scenes. Journal of Vision 8, 1–25.CrossRefGoogle ScholarPubMed
Ziou, D. & Tabbone, S. (1998). Edge detection techniques—An overview. International Journal of Pattern Recognition and Image Analysis 8, 537–559.Google Scholar