lecture 23 imaging color cap 5415 n.
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Lecture 23: Imaging, Color CAP 5415. Working with Intensity Values. Simple Non-Linearities can have a great effect on image appearance. Working with Intensity Values. Simple Non-Linearities can have a great effect on image appearance. Working with Intensity Values.

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working with intensity values
Working with Intensity Values
  • Simple Non-Linearities can have a great effect on image appearance
working with intensity values1
Working with Intensity Values
  • Simple Non-Linearities can have a great effect on image appearance
working with intensity values2
Working with Intensity Values
  • Simple Non-Linearities can have a great effect on image appearance
working with intensity values3
Working with Intensity Values
  • This is not just linear scaling
working with intensity values4
Working with Intensity Values
  • Can go the other way
gamma correction
Gamma correction
  • Model this non-linearity using this type of curve:
cameras and gamma
Cameras and Gamma
  • Consumer cameras put a similar non-linearity into photos
  • Makes images look better
  • You (as a vision person) don't want nice-looking images
  • You want ACCURATE images
  • Need to calibrate camera to remove gamma
which leads to a second problem
Which leads to a second problem
  • Most camera only give you 8 bits of precision
  • Hard to capture both bright and dim regions
  • Solution:
    • Use multiple images to create a High Dynamic Range Image
  • Can be solved as a least-squares problem if you account for saturation and dimness
now that we can get accurate intensities what about color
Now that we can get accurate intensities, what about color?
  • Engineering problem:
    • I have sensor that records the amount of light at different pixels
    • How do I get a color image instead of a black and white image?
solutions
Solutions
  • Three sensors
  • One sensor with a color mask
    • Each pixel records one wavelength
  • A common pattern for the mask is the Bayer pattern:
mosaicing
Mosaicing
  • So, if I took a picture of this edge
  • My sensor would record this image
demosaicing
Demosaicing
  • I have 1 color at each pixel
  • I need three
  • Easy solution: Interpolate

+

problem this smooths across the edge
Problem! This smooths across the edge
  • Because the different pixels are used to red and green, the smoothing may be different

+

color fringing
Color Fringing

(Results from Brainard et al)

fast solution
Fast Solution
  • The fringing occurs when the correlation between the color channels is incorrectly estimated
  • One measure of this correlation is the color difference
  • Can fix errors using median filtering
simple demosaicing algorithm freeman
Simple Demosaicing Algorithm (Freeman)
  • Use linear interpolation to get first estimate
  • Compute difference images between color channels
  • Median filter these difference images
  • Use filtered difference images to reconstruct
color matching experiments i
Color matching experiments - I
  • Show a split field to subjects; one side shows the light whose color one wants to measure, the other a weighted mixture of primaries (fixed lights).
  • Each light is seen in film color mode.

(Slide by Forsyth)

color matching experiments ii
Color matching experiments - II
  • Many colors can be represented as a mixture of A, B, C
  • write M=a A + b B + c C

where the = sign should be read as “matches”

  • This is additive matching.
  • Gives a color description system - two people who agree on A, B, C need only supply (a, b, c) to describe a color.

(Slide by Forsyth)

subtractive matching
Subtractive matching
  • Some colors can’t be matched like this: instead, must write

M+a A = b B+c C

  • This is subtractive matching.
  • Interpret this as (-a, b, c)
  • Problem for building monitors:
    • Choose R, G, B such that positive linear combinations match a large set of colors

(Slide by Forsyth)

the principle of trichromacy
The principle of trichromacy
  • Experimental facts:
    • Three primaries will work for most people if we allow subtractive matching
      • Exceptional people can match with two or only one primary.
      • This could be caused by a variety of deficiencies.
    • Most people make the same matches.
      • There are some anomalous trichromats, who use three primaries but make different combinations to match.

(Slide by Forsyth)

grassman s laws
Grassman’s Laws
  • For colour matches made in film colour mode:
    • symmetry: U=V <=>V=U
    • transitivity: U=V and V=W => U=W
    • proportionality: U=V <=> tU=tV
    • additivity: if any two (or more) of the statements

U=V,

W=X,

(U+W)=(V+X) are true, then so is the third

  • These statements are as true as any biological law. They mean that color matching in film color mode is linear.
linear color spaces
RGB: primaries are monochromatic energies are 645.2nm, 526.3nm, 444.4nm.

CIE XYZ: Primaries are imaginary, but have other convenient properties. Color coordinates are (X,Y,Z), where X is the amount of the X primary, etc.

Usually draw x, y, where x=X/(X+Y+Z) y=Y/(X+Y+Z)

Linear color spaces
  • A choice of primaries yields a linear color space --- the coordinates of a color are given by the weights of the primaries used to match it.
  • Choice of primaries is equivalent to choice of color space.
perceptually linear color spaces
Perceptually Linear Color Spaces
  • When doing thing like segmentation or edge detection, we would like to compare colors in a way that is similar to how humans judge similarity
  • A perceptually-linear space tries to match the numerical distances to our perceptual differences.
cie l a b
CIE L*a*b*
  • Can be computed from CIE XYZ Space
  • What about RGB?
    • Need a standard for what R,G,and B are
    • sRGB or Adobe RGB are examples