Hoip10 presentacion cambios de color_univ_granada

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Presentación de la Universidad de Granada sobre cambios de color en escenarios naturales debidos a la interacción entre luz y atmósfera, realizada durante las jornadas HOIP 2010 organizadas por la Unidad de Sistemas de Información e Interacción TECNALIA.

Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm

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Hoip10 presentacion cambios de color_univ_granada

  1. 1. COLOR CHANGES IN A NATURAL SCENE DUE TO THE INTERACTION BETWEEN THE LIGHT  AND THE ATMOSPHERE Colour Imaging Laboratory Department of Optics University of Granada (SPAIN)
  2. 2. COLOR CHANGES IN A NATURAL SCENE  Javier Romero DUE TO THE INTERACTION BETWEEN THE  Professor LIGHT AND THE ATMOSPHERE Juan L. Nieves Associate Professor • Motivation and State of the Art • Physical model • ExperimentJavier Hernández-Andrés Associate Professor • Colour changes with distance • Conclusions and future work Raúl Luzón Ph.D. student
  3. 3. Motivationsize decreasesspatial frequency increasesblur increases ce tan di s
  4. 4. Motivation • Single Scattering : ( Mie 1908 )Light is degraded due Incidentto its interaction with Beammolecules and particles Size: 0.01 μm Size: 0.1 μm Size: 1 μmin the atmosphere. • Multiple Scattering :Degradation depends First Order Third Orderon the range (distance) Incident Beamand on the wavelength. Second Order
  5. 5. MotivationLight is degraded dueto its interaction withmolecules and particlesin the atmosphere.* reduction in visibility and contrast* color changes: -less saturated colors, -hue change, Reversibility?
  6. 6. MotivationColor, size, shape,texture are the main Foggy Day Imagefeatures for patternrecognition......in addition to spectral Clear Day Imageinformation which caninfluence surveillance Are color and spectraland identification. degradation reversible? “De-weathering” images?
  7. 7. State of the ArtCurrent image enhancement algorithms1) Non-physics-based algorithms: • Based on statistical information of the image, • ... using no information about the imaging physics. 2) Physics-based models: • Using the underlying physics of the atmospheric degradation process... • ...and then to compensate for it with appropriate image processing.
  8. 8. State of the Art1) Based on statistical information of the scene: Histogram equalization and its variations (Pitas and Kiniklis [1996], Pizer et al. [1987]). •RGB channels as separate channels •Certain improvement on HSI space. Advantages Drawbacks Straightforward technique False colors Not intensive computation Undesirable effects Increase the global contrast Histogram Original equalized
  9. 9. State of the Art2) Physics-based models: Light interaction with particles and molecules of different sizes in the atmosphere: • Absorption-Emission; • Scattering:-Attenuation -Airlight McCartney [1976]
  10. 10. State of the Art2) Physics-based models: The best physical based models are those constructed over the dichromatic atmospheric scattering model (Tan and Oakley [2001], Narasimhan and Nayar [2000]). These models are based on single-scattering. Assuming the same β for all color channels… Narasimhan and Nayar (2003) …the color of a scene point is a linear combination of the direction of airlight and the direction of direct transmission (attenuated by scattering)
  11. 11. State of the Art2) Physics-based models: Advantages Drawbacks Exploit the underlying Usually needs informationphysics of the degradation about meteorological process conditions Good color recuperation Some images taken under different weather conditions Applicable for different Identify some points on the distances scene Simplification of real process
  12. 12. State of the ArtOriginal Enhanced with physical model RGB HSI From Tan and Oakley [2001]
  13. 13. Our goalSimple and fast algorithm to recover colorinformation (and spectral information)... for cleardays and overcast days.Only one image: no distance information, and noscattering coefficients values.But, we need first to analyze and to quantify thecolor changes due to the atmosphere.
  14. 14. • Motivation and State of the Art• Physical model• Experiment• Colour changes with distance• Conclusions and future work
  15. 15. Physical ModelThe irradiance (E) in one pixel is proportional to the radianceof the scene (L), assuming there is no absorption and λreflection inside the camera E ( ) = ΩL( ) λ For perfect Lambertian surfaces ρ ( λ ) Ed ( λ ) LO (λ ) = π
  16. 16. Physical ModelRadiance from the object at the camera plane has two terms(Narasimhan and Nayar [2000], [2003]): • one due to direct light coming from the object and attenuated by the atmosphere • other term: airlight − βtot ( λ ) d − βtot ( λ ) d L(λ ) = L0 (λ )e + L∞ (λ )(1 − e ) Direct light Airlight where: L is the object radiance viewed from the observer plane L0 is the object radiance βtot = βsct + βabs , is the attenuation coefficient in the atmosphere L∞ is the radiance of the horizon d is the distance between the object and the detector λ is the wavelength
  17. 17. Physical ModelFor clear skies, a Lambertian object receiving anirradiance Ed produces an irradiance on the detector : Ed (λ ) ρ (λ ) − βtot ( λ ) d − βtot ( λ ) dEt (λ ) = Ω e + ΩL∞ (λ )(1 − e ) πwhere: Ω is the solid angle subtended from the object into thedetector Ed is the irradiance over the object ρ is the spectral reflectance of the object βtot is the attenuation coefficient d is the distance between the object and the detector L∞ is the horizon radiance λ is the wavelength
  18. 18. Physical Model For overcast skies, assuming an homogeneous distribution of the sky radiance [Gordon and Church [1966]) and a Lambertian object:Et ( λ) =ΩL∞ ( λ) ρ ( λ) e −βtot ( λ) d ( +ΩL∞ ( λ) 1− e −βtot ( λ) d ) where: Ω is the solid angle subtended from the object into the detector ρ is the spectral reflectance of the object βtot is the attenuation coefficient d is the distance between the object and the detector L∞ is the horizon radiance λ is the wavelength
  19. 19. • Motivation and State of the Art• Physical model• Experiment• Colour changes with distance• Conclusions and future work
  20. 20. ExperimentColor changesCIE 1931 (x,y,Y) and CIELAB (L*,a*,b*) valuescorresponding to 240 objects of the GretagMacbethColor-Checker DC, whose spectral reflectances areknown GretagMacbeth SpectraScan PR-650 ColorChecker DC spectroradiometer
  21. 21. Experiment
  22. 22. Experiment
  23. 23. Experiment Ed (λ ) ρ (λ ) − βtot ( λ ) d − βtot ( λ ) dEt (λ ) = Ω e + ΩL∞ (λ )(1 − e ) πWe know the scattering coefficient at 450, 550 and 700nm and we can interpolate to the rest of visible spectrumassuming that (McCartney [1976]): 1 β sct = cte λ uAnother assumption: the absorption coefficient is constantin the visible range.
  24. 24. Experiment 1β sct = cte λ u Day βsct(550 nm) Mm-1 βabs(670 nm) Mm-1 u 15/March/2010 (dust) 50.21 7.83 1.79 16/March2010 (clear) 42.06 17.78 1.89 19/March/2010 (dust) 100.04 51.08 0.37 16/April/2010 (overcast) 80.60 40.95 1.88 20/April/2010 (overcast) 62.26 43.66 1.93 28/April/2010 (clear) 56.76 65.44 1.59
  25. 25. • Motivation and State of the Art• Physical model• Experiment• Colour changes with distance• Conclusions and future work
  26. 26. Colour changes in the object with observation distanceSix days240 objectsDistances from 0 to many km
  27. 27. Colour changes in the object with observation distance
  28. 28. Colour changes in the object with observation distance
  29. 29. Colour changes in the object with observation distanceDirect light from theobject is attenuated with the distance For a specific distance, airlight becomes more important.
  30. 30. Colour changes in the object with observation distance
  31. 31. Colour changes in the object with observation distance20/Apr/2010Overcast day
  32. 32. Colour changes in the object with observation distanceCIELAB
  33. 33. Colour changes in the object with observation distanceCIELAB Are these colour changes reversible? Are we able to enhance visibility for better identification?…if so, some kind of colour constancycould be achieved.
  34. 34. ...and what does “color constancy” mean?Colour appearance can chage dramatically underdifferent illumination conditions… …finding both a color mapping and the color of the scene illuminant are equivalent problems.
  35. 35. ...and what does “color constancy” mean?Colour appearance can chage dramatically underdifferent illumination conditions… CCT = 2760K Incandescent lamp CCT = 5190K Day-light …but the human visual system is able to ☺ compensate for those chages.
  36. 36. ...and what does “color constancy” mean?Cones excitations changeregularly with illumination What about the images degradated by the atmosphere?
  37. 37. ...and what does “color constancy” mean? For a particular object: L viewed under different distances versus L under the E illuminant (flat spectrum) Same for M and S cones or for just R, G, B Ed (λ ) ρ (λ ) − βtot ( λ ) d − βtot ( λ ) d Et (λ ) = Ω e + ΩL∞ (λ )(1 − e ) πClear daysEt ( λ) =ΩL∞ ( λ) ρ ( λ) eOvercast days −βtot ( λ) d ( +ΩL∞ ( λ) 1− e −βtot ( λ) d )
  38. 38. ...and what does “color constancy” mean? 20 objects from the Color Checker For a zero distance we should expect a linear relation: Other distances?L Other cones (M or S)? Other broad band sensors (R,G,B)? LE
  39. 39. ...and what does “color constancy” mean?20 objects from the Color CheckerFor a zero distance we should expect alinear relation: Other distances? Other cones (M or S)? Other broad band sensors (R,G,B)?
  40. 40. Conclusions and future work It`s clear that visibility of objects depends on weatherconditions and changes in the objects’ color caninfluence identification. Colour constancy approaches could be ? applied in bad weather conditions to restore the colour appearance of objects.
  41. 41. Javier Romero Professor Thank you for your Juan L. Nieves attention! Associate ProfessorJavier Hernández-Andrés Associate Professor Raúl Luzón Ph.D. student
  42. 42. References1. W. E. K. Middleton, “Vision through the atmosphere”, 2nd Edition, University of Toronto Press, 19522. I. Pitas and P. Kiniklis, “Multichannel Techniques in Color Image Enhancement and Modeling”, ImageProcessing, IEEE Transactions, Vol 5,No. 1, pp. 168-171, 1996.3. Stephen M. Pizer, E. Philip Amburn, John D. Austin, Robert Cromartie, Ari Geselowitz, Trey Greer,Bart ter Haar Romeny, John B. Zimmerman and Karel Zuiderveld, “Adaptive histogram equalization andits variations”, Computer Vision, Graphics and Image Processing Vol 39, 355-368, 1987.4. K. Tan and J.P. Oakley, “Physics-Based Approach to Color Image Enhancement in Poor VisibilityConditions”, Journal of the Optical Society of America, Vol. 18, No. 10, pp. 2460-2467, 2001.5. S. G. Narasimhan and S. K. Nayar, “Chromatic Framework for Vision in Bad Weather”, Conference onComputer Vision and Pattern Recognition, IEEE Proceedings. Vol. 1, pp. 598-605, 2000.6. S. G. Narasimhan and S. K. Nayar, “Contrast Restoration of Weather Degraded Images”, PatternAnalysis And Machine Intelligence, IEEE Transactions, Vol. 25, No. 6, pp. 713-724, 2003.7. S. G. Narasimhan and S. K. Nayar, “Vision in Bad Weather”, Seventh IEEE International Conference inComputer Vision, IEEE Proceedings, Vol 1, pp. 820-827, 2000.8. Earl J. McCartney, “Optics of the atmosphere, scattering by molecules and particles”, Wiley-Interscience, 1976.9. Nascimento SMC, Ferreira FP, Foster DH. “Statistic of spatial cone excitation ratios in natural scenes.J Opt Soc Am A ;19:1484–1490 (2002).

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