03 - Light, Color, Eyes, And Pixels

8/10/2019 03 - Light, Color, Eyes, And Pixels

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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 Whats the film?

photoreceptor cells (rods and cones) in the retina

Slide by Steve Seitz


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The Retina

Cross-section of eye

Ganglion cell layerBipolar cell layer

Receptor layer

Pigmentedepithelium

Ganglion axons

Cross section of retina


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What humans dont have: tapetum lucidum


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Rod / Cone sensitivity

The famous sock- matching problem


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Electromagnetic Spectrum

http://www.yorku.ca/eye/photopik.htm

Human Luminance Sensitivity Function


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Why do we see light of these wavelengths?

Stephen E. Palmer, 2002

because thats where the

Sun radiates EM energy

Visible Light


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The Physics of Light

Any patch of light can be completely describedphysically by its spectrum: the number of photons(per time unit) at each wavelength 400 - 700 nm.

400 500 600 700

Wavelength (nm.)

# Photons(per ms.)



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.

#Photons

D. Normal Daylight

Wavelength (nm.)

B. Gallium Phosphide Crystal

400 500 600 700

#P

hotons

Wavelength (nm.)

A. Ruby Laser

400 500 600 700

400 500 600 700

#Photons

C. Tungsten Lightbulb

400 500 600 700

#P

hotons

Some examples of the spectra of light sources



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Some examples of the reflectance spectra of surfaces

Wavelength (nm)

% P

h o t o n s

R e

f l e c t e

d

Red

400 700

Yellow

400 700

Blue

400 700

Purple

400 700



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.

400 450 500 550 600 650RELATIVE ABSORBANCE (%)

WAVELENGTH (nm.)

100

50

440

S

530 560 nm.

M L

Three kinds of cones:

Physiology of Color Vision

Why are M and L cones so close? Why are there 3?


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Tetrachromatism

Most birds, and many other animals, havecones for ultraviolet light.

Some humans, mostly female, seem to haveslight tetrachromatism.

Bird coneresponses


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Image Formation


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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) and CMOS http://electronics.howstuffworks.com/digital-camera.htm

http://electronics.howstuffworks.com/digital-camera.htmhttp://electronics.howstuffworks.com/digital-camera.htmhttp://electronics.howstuffworks.com/digital-camera.htm


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Sensor Array

CMOS sensor


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The raster image (pixel matrix)


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The raster image (pixel matrix)0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92

0.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.990.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93


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Color Images: Bayer Grid

Estimate RGB

at G cells fromneighboringvalues

http://www.cooldictionary.com/words/Bayer-filter.wikipedia



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Color ImageR

G

B


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Images in Matlab Images represented as a matrix Suppose we have a NxM RGB image called im

im(1,1,1) = top-left pixel value in R-channel im(y, x, b) = y pixels down, x pixels to right in the b th channel im(N, M, 3) = bottom-right pixel in B-channel

imread(filename) returns a uint8 image (values 0 to 255) Convert to double format (values 0 to 1) with im2double

0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.920.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.99

0.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93

0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92

0.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.990.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93

0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.920.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.990.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93

R

G

B

row column


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Color spaces

How can we represent color?

http://en.wikipedia.org/wiki/File:RGB_illumination.jpg


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Color spaces: HSV

Intuitive color space

H(S=1,V=1)

S(H=1,V=1)

V(H=1,S=0)


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Color spaces: YCbCr

Y(Cb=0.5,Cr=0.5)

Cb(Y=0.5,Cr=0.5)

Cr (Y=0.5,Cb=05)

Y=0 Y=0.5

Y=1Cb

Cr

Fast to compute, good forcompression, used by TV


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Color spaces: L*a*b*

Perceptually uniform * color space

L(a=0,b=0)

a(L=65,b=0)

b(L=65,a=0)


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If you had to choose, would you rather gowithout luminance or chrominance?


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If you had to choose, would you rather gowithout luminance or chrominance?


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Most information in intensity

Only color shown constant intensity


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Only intensity shown constant color


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Original image


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Back to grayscale intensity0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92

0.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.990.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93


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Next classes: filtering!

Image filters in spatial domain Filter is a mathematical operation of a grid of numbers Smoothing, sharpening, measuring texture

Image filters in the frequency domain Filtering is a way to modify the frequencies of images Denoising, sampling, image compression

Templates and Image Pyramids Filtering is a way to match a template to the image Detection, coarse-to-fine registration

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03 - Light, Color, Eyes, And Pixels