1. Capturing Light… in man and machine 15-463: Computational Photography Alexei Efros, CMU, Fall 2006 Some figures from Steve Seitz, Steve Palmer, Paul Debevec, and Gonzalez et al.

2. Image Formation Digital Camera The Eye Film

3. Digital camera A digital camera replaces film with a sensor array Each cell in the array is light-sensitive diode that converts photons to electrons Two common types –Charge Coupled Device (CCD) –CMOS http://electronics.howstuffworks.com/digital-camera.htm

4. Sensor Array CMOS sensor

5. Sampling and Quantization

6. Interlace vs. progressive scan http://www.axis.com/products/video/camera/progressive_scan.htm

7. Progressive scan http://www.axis.com/products/video/camera/progressive_scan.htm

8. Interlace http://www.axis.com/products/video/camera/progressive_scan.htm

9. The Eye The human eye is a camera! Iris - colored annulus with radial muscles Pupil - the hole (aperture) whose size is controlled by the iris What’s the “film”? –photoreceptor cells (rods and cones) in the retina

10. The Retina

11. Retina up-close Light

12. © Stephen E. Palmer, 2002 Cones cone-shaped less sensitive operate in high light color vision Two types of light-sensitive receptors Rods rod-shaped highly sensitive operate at night gray-scale vision

13. Rod / Cone sensitivity The famous sock-matching problem…

14. © Stephen E. Palmer, 2002 Distribution of Rods and Cones Night Sky: why are there more stars off-center?

15. Electromagnetic Spectrum http://www.yorku.ca/eye/photopik.htm Human Luminance Sensitivity Function

16. Why do we see light of these wavelengths? © Stephen E. Palmer, 2002 …because that’s where the Sun radiates EM energy Visible Light

17. The Physics of Light Any patch of light can be completely described physically by its spectrum: the number of photons (per time unit) at each wavelength 400 - 700 nm. © Stephen E. Palmer, 2002

18. The Physics of Light Some examples of the spectra of light sources © Stephen E. Palmer, 2002

19. The Physics of Light Some examples of the reflectance spectra of surfaces Wavelength (nm) % Photons Reflected Red 400 700 Yellow 400 700 Blue 400 700 Purple 400 700 © Stephen E. Palmer, 2002

20. The Psychophysical Correspondence There is no simple functional description for the perceived color of all lights under all viewing conditions, but …... A helpful constraint: Consider only physical spectra with normal distributions area mean variance © Stephen E. Palmer, 2002

21. The Psychophysical Correspondence MeanHue # Photons Wavelength © Stephen E. Palmer, 2002

22. The Psychophysical Correspondence VarianceSaturation Wavelength # Photons © Stephen E. Palmer, 2002

23. The Psychophysical Correspondence AreaBrightness # Photons Wavelength © Stephen E. Palmer, 2002

24. Three kinds of cones: Physiology of Color Vision Why are M and L cones so close? Are are there 3?

25. More Spectra metamers

26. Color Sensing in Camera (RGB) 3-chip vs. 1-chip: quality vs. cost Why more green? http://www.coolditionary.com/words/Bayer-filter.wikipedia http://www.cooldictionary.com/words/Bayer-filter.wikipedia Why 3 colors?

27. Practical Color Sensing: Bayer Grid Estimate RGB at ‘G’ cels from neighboring values http://www.cooldictionary.com/ words/Bayer-filter.wikipedia

28. RGB color space RGB cube Easy for devices But not perceptual Where do the grays live? Where is hue and saturation?

29. HSV Hue, Saturation, Value (Intensity) RGB cube on its vertex Decouples the three components (a bit) Use rgb2hsv() and hsv2rgb() in Matlab

30. Programming Assignment #1 How to compare R,G,B channels? No right answer Sum of Squared Differences (SSD): Normalized Correlation (NCC):

31. Image Pyramids (preview) Known as a Gaussian Pyramid [Burt and Adelson, 1983] In computer graphics, a mip map [Williams, 1983] A precursor to wavelet transform

32. White Balance White World / Gray World assumptions

33. Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]

34. Problem: Dynamic Range 1500 1 1 25,000 400,000 2,000,000,000 The real world is High dynamic range

35. pixel (312, 284) = 42 Image 42 photos? Is Camera a photometer?

36. Long Exposure 10 -6 10 6 10 -6 10 6 Real world Picture 0 to 255 High dynamic range

37. Short Exposure 10 -6 10 6 10 -6 10 6 Real world Picture 0 to 255 High dynamic range

38. sceneradiance (W/sr/m ) sceneradiance  sensorirradiancesensorirradiancesensorexposuresensorexposure LensLensShutterShutter 22 tttt analog voltages analog voltages digital values digital values pixel values pixel values CCD ADC Remapping Image Acquisition Pipeline Camera is NOT a photometer!

39. Varying Exposure

40. What does the eye sees? The eye has a huge dynamic range Do we see a true radiance map?

41. Eye is not a photometer! "Every light is a shade, compared to the higher lights, till you come to the sun; and every shade is a light, compared to the deeper shades, till you come to the night." — John Ruskin, 1879

42. Cornsweet Illusion

43. Campbell-Robson contrast sensitivity curve Sine wave

44. Metamers Eye is sensitive to changes (more on this later…)