Capturing Light in man and machine
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1 Capturing Light in man and machine : Computational Photography Alexei Efros, CMU, Fall 2010
2 Etymology PHOTOGRAPHY light drawing / writing
3 Image Formation Digital Camera Film The Eye
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 Stephen E. Palmer, 2002
13 Rod / Cone sensitivity The famous sock-matching problem
14 . Distribution of Rods and Cones # Receptors/mm2 150, ,000 50, Rods 60 Cones 40 Fovea 20 0 Blind Spot Rods Cones Visual Angle (degrees from fovea) Night Sky: why are there more stars off-center? Stephen E. Palmer, 2002
15 Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995
16 Electromagnetic Spectrum Human Luminance Sensitivity Function
17 Visible Light Why do we see light of these wavelengths? because that s where the Sun radiates EM energy Stephen E. Palmer, 2002
18 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
19 . # Photons # Photons # Photons # Photons The Physics of Light Some examples of the spectra of light sources A. Ruby Laser B. Gallium Phosphide Crystal Wavelength (nm.) Wavelength (nm.) C. Tungsten Lightbulb D. Normal Daylight Stephen E. Palmer, 2002
20 % Photons Reflected The Physics of Light Some examples of the reflectance spectra of surfaces Red Yellow Blue Purple Wavelength (nm) Stephen E. Palmer, 2002
21 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
22 # Photons The Psychophysical Correspondence Mean Hue blue green yellow Wavelength Stephen E. Palmer, 2002
23 # Photons The Psychophysical Correspondence Variance Saturation hi. high med. low medium low Wavelength Stephen E. Palmer, 2002
24 # Photons The Psychophysical Correspondence Area Brightness B. Area Lightness bright dark Wavelength Stephen E. Palmer, 2002
25 . RELATIVE ABSORBANCE (%) Physiology of Color Vision Three kinds of cones: nm. 100 S M L WAVELENGTH (nm.) Why are M and L cones so close? Why are there 3? Stephen E. Palmer, 2002
26 More Spectra metamers
27 Color Constancy The photometer metaphor of color perception: Color perception is determined by the spectrum of light on each retinal receptor (as measured by a photometer). Stephen E. Palmer, 2002
28 Color Constancy The photometer metaphor of color perception: Color perception is determined by the spectrum of light on each retinal receptor (as measured by a photometer). Stephen E. Palmer, 2002
29 Color Constancy The photometer metaphor of color perception: Color perception is determined by the spectrum of light on each retinal receptor (as measured by a photometer). Stephen E. Palmer, 2002
30 Color Constancy Do we have constancy over all global color transformations? 60% blue filter Complete inversion Stephen E. Palmer, 2002
31 Color Constancy Color Constancy: the ability to perceive the invariant color of a surface despite ecological Variations in the conditions of observation. Another of these hard inverse problems: Physics of light emission and surface reflection underdetermine perception of surface color Stephen E. Palmer, 2002
32 Camera White Balancing Manual Choose color-neutral object in the photos and normalize Automatic (AWB) Grey World: force average color of scene to grey White World: force brightest object to white
33 Color Sensing in Camera (RGB) 3-chip vs. 1-chip: quality vs. cost Why more green? Why 3 colors? Slide by Steve Seitz
34 Practical Color Sensing: Bayer Grid Estimate RGB at G cels from neighboring values words/bayer-filter.wikipedia Slide by Steve Seitz
35 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
36 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
37 Programming Project #1 How to compare R,G,B channels? No right answer Sum of Squared Differences (SSD): Normalized Correlation (NCC):
38 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 Can use imresize in Matlab
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