Capturing Light in man and machine

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1 Capturing Light in man and machine : Computational Photography Alexei Efros, CMU, Fall 2008

2 Image Formation Digital Camera Film The Eye

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 Slide by Steve Seitz

4 Sensor Array CMOS sensor

5 Sampling and Quantization

6 Interlace vs. progressive scan Slide by Steve Seitz

7 Progressive scan Slide by Steve Seitz

8 Interlace Slide by Steve Seitz

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 Slide by Steve Seitz

10 The Retina Cross-section of eye Cross section of retina Ganglion axons Ganglion cell layer Bipolar cell layer Pigmented epithelium Receptor layer

11 Retina up-close Light

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

13 Rod / Cone sensitivity The famous sock-matching problem

14 Distribution of Rods and Cones # Receptors/mm2 150, ,000 Rods Fovea Blind Spot Rods 50,000 Cones Cones Visual Angle (degrees from fovea) Night Sky: why are there more stars off-center? Stephen E. Palmer, 2002

15 Electromagnetic Spectrum Human Luminance Sensitivity Function

16 . Visible Light Why do we see light of these wavelengths? C Energy 5000 C because that s where the Sun radiates EM energy 2000 C 700 C Visible Region Wavelength (nm) Stephen E. Palmer, 2002

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 nm. # Photons (per ms.) Wavelength (nm.) Stephen E. Palmer, 2002

18 The Physics of Light Some examples of the spectra of light sources A. Ruby Laser B. Gallium Phosphide Crystal Wavelength (nm.) D. Normal Daylight # Photons # Photons Wavelength (nm.) C. Tungsten Lightbulb # Photons # Photons Stephen E. Palmer, 2002

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

21 The Psychophysical Correspondence Mean Hue # Photons blue green yellow Wavelength Stephen E. Palmer, 2002

22 The Psychophysical Correspondence Variance Saturation # Photons hi. med. low high medium low Wavelength Stephen E. Palmer, 2002

23 The Psychophysical Correspondence Area Brightness B. Area Lightness # Photons bright dark Wavelength Stephen E. Palmer, 2002

24 Physiology of Color Vision Three kinds of cones: nm. RELATIVE ABSORBANCE (%) 100 S M L WAVELENGTH (nm.) Why are M and L cones so close? Are are there 3? Stephen E. Palmer, 2002

25 More Spectra metamers

26 Color Sensing in Camera (RGB) 3-chip vs. 1-chip: quality vs. cost Why more green? Why 3 colors? tionary.com/words/bayer-filter.wikipedia Slide by Steve Seitz

27 Practical Color Sensing: Bayer Grid Estimate RGB at G cels from neighboring values words/bayer-filter.wikipedia Slide by Steve Seitz

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

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

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

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