Recap from Friday Pinhole camera model Perspective projections Lenses and their flaws Focus Depth of field Focal length and field of view

What is wrong with this picture?

What is wrong with this picture?

Capturing Light… in man and machine Many slides by Alexei A. Efros CS 195g: Computational Photography James Hays, Brown, Spring 2010

Image Formation Digital Camera Film The Eye

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 Slide by Steve Seitz

Sensor Array CMOS sensor

Sampling and Quantization

Interlace vs. progressive scan http://www.axis.com/products/video/camera/progressive_scan.htm Slide by Steve Seitz

Progressive scan http://www.axis.com/products/video/camera/progressive_scan.htm Slide by Steve Seitz

Interlace http://www.axis.com/products/video/camera/progressive_scan.htm Slide by Steve Seitz

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

The Retina

Retina up-close Tapetum lucidum Light

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

Rod / Cone sensitivity The famous sock-matching problem…

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

Eye Movements Saccades Microsaccades Ocular microtremor (OMT)

Electromagnetic Spectrum At least 3 spectral bands required (e.g. R,G,B) Human Luminance Sensitivity Function http://www.yorku.ca/eye/photopik.htm

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

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

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

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

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 area variance © Stephen E. Palmer, 2002

The Psychophysical Correspondence Mean Hue # Photons Wavelength © Stephen E. Palmer, 2002

The Psychophysical Correspondence Variance Saturation Wavelength # Photons © Stephen E. Palmer, 2002

The Psychophysical Correspondence Area Brightness # Photons Wavelength © Stephen E. Palmer, 2002

Physiology of Color Vision Three kinds of cones: Why are M and L cones so close? Why are there 3? © Stephen E. Palmer, 2002

More Spectra metamers

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

Practical Color Sensing: Bayer Grid Estimate RGB at ‘G’ cells from neighboring values http://www.cooldictionary.com/ words/Bayer-filter.wikipedia Slide by Steve Seitz

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

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

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

Image half-sizing This image is too big to fit on the screen. How can we reduce it? How to generate a half- sized version?

Image sub-sampling 1/8 1/4 Throw away every other row and column to create a 1/2 size image - called image sub-sampling Slide by Steve Seitz

Image sub-sampling 1/2 1/4 (2x zoom) 1/8 (4x zoom) Aliasing! What do we do? Slide by Steve Seitz

Gaussian (lowpass) pre-filtering Solution: filter the image, then subsample Filter size should double for each ½ size reduction. Why? Slide by Steve Seitz

Subsampling with Gaussian pre-filtering Slide by Steve Seitz

Compare with... 1/2 1/4 (2x zoom) 1/8 (4x zoom) Slide by Steve Seitz

Gaussian (lowpass) pre-filtering Solution: filter the image, then subsample Filter size should double for each ½ size reduction. Why? How can we speed this up? Slide by Steve Seitz

Image Pyramids In computer graphics, a mip map [Williams, 1983] Known as a Gaussian Pyramid [Burt and Adelson, 1983] In computer graphics, a mip map [Williams, 1983] A precursor to wavelet transform Slide by Steve Seitz

A bar in the big images is a hair on the zebra’s nose; in smaller images, a stripe; in the smallest, the animal’s nose Figure from David Forsyth

What are they good for? Improve Search Pre-computation Search over translations Like project 1 Classic coarse-to-fine strategy Search over scale Template matching E.g. find a face at different scales Pre-computation Need to access image at different blur levels Useful for texture mapping at different resolutions (called mip-mapping)

Gaussian pyramid construction filter mask Repeat Filter Subsample Until minimum resolution reached can specify desired number of levels (e.g., 3-level pyramid) The whole pyramid is only 4/3 the size of the original image! Slide by Steve Seitz