# Color to Binary Vision The assignment More challenging Extra

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CSE190-B, Spring 2003                                Computer Vision   CSE190-B, Spring 2003                                         Computer Vision

The assignment                                            More challenging (Extra Credit)
Irfanview: A good utility
Two parts:                    For images
1. Color classification
2. Binary image processing

Label
• Red pixels as 1
• Green Pixels as 2
• Black pixels as 0

CSE190-B, Spring 2003                                Computer Vision   CSE190-B, Spring 2003                                         Computer Vision

Other part: Binary Image Proccessing                                                          Lighting
(Next lecture)
• Applied lighting can be represented as a
function on the 4-D ray space (radiances)

• Special light sources
– Point sources
– Distant point sources
– Strip sources
– Area sources

CSE190-B, Spring 2003                                Computer Vision   CSE190-B, Spring 2003                                         Computer Vision
Camera’s sensor                                                                                    BRDF
• Measured pixel intensity is a function of irradiance                     • Bi-directional Reflectance
integrated over                                                            Distribution Function
– pixel’s area
– over a range of wavelengths
ρ(θin, φin ; θout, φout)
– For some time
• Function of
• Ideally, it’s proportional to the radiance.                                   – Incoming light direction:                              (θin,φin)
^
n
θin , φin
– Outgoing light direction:
θout , φout
I = ∫∫∫∫ E(x, y, λ, t )s(x, y)q(λ)dydxd dt
λ                                                                                                                (θout,φout)
t λ x y                                                       • Ratio of incident irradiance to

CSE190-B, Spring 2003                                           Computer Vision   CSE190-B, Spring 2003                                                                           Computer Vision

The appearance of colors
Robot Soccer
• Color appearance is strongly affected by (at
least):
–   Spectrum of lighting striking the retina
–    other nearby colors (space)
–   adaptation to previous views (time)
–   “state of mind”

CSE190-B, Spring 2003                                           Computer Vision   CSE190-B, Spring 2003                                                                           Computer Vision

Light Spectrum                                                                    Color Reflectance
Measured color spectrum is
a function of the spectrum
of the illumination and
reflectance

From Foundations of Vision, Brian Wandell, 1995, via B. Freeman slides
CSE190-B, Spring 2003                                           Computer Vision   CSE190-B, Spring 2003                                                                           Computer Vision
Illumination Spectra                                                                                     Color receptors

Blue skylight                                     Tungsten bulb

“Red” cone      “Green” cone           “Blue” cone

Response of k’th cone =
∫ρ    k
(λ ) E (λ ) dλ

From Foundations of Vision, Brian Wandell, 1995, via B. Freeman slides
CSE190-B, Spring 2003                                                                    Computer Vision   CSE190-B, Spring 2003                                            Computer Vision

RGB Color Cube                                                                                       Color spaces
• Block of colours for (r, g,                     • Linear color spaces describe        • RGB: primaries are
b) in the range (0-1).                            colors as linear combinations         monochromatic, energies are
• Convenient to have an                             of primaries                          645.2nm, 526.3nm, 444.4nm.
upper bound on                                                                          Color matching functions have
• Choice of primaries=choice of         negative parts -> some colors
coefficient of each                               color matching
primary.                                                                                can be matched only
functions=choice of color             subtractively.
• In practice:                                      space                               • CIE XYZ: Color matching
– primaries given by monitor
phosphors                                 • Color matching functions,             functions are positive
– (phosphors are the materials                hence color descriptions, are         everywhere, but primaries are
on the face of the monitor                  all within linear                     imaginary. Usually draw x, y,
screen that glow when
transformations                       where x=X/(X+Y+Z)
struck by electrons)                                                                   y=Y/(X+Y+Z)

CSE190-B, Spring 2003                                                                    Computer Vision   CSE190-B, Spring 2003                                            Computer Vision

Color Matching                                                                              Color matching functions
• Choose primaries, say A, B, C
• Given energy function, E(λ )
what amounts of primaries will          Then our match is:
match it?
• For each wavelength λ, determine
how much of A, of B, and of C is          { a(λ )E(λ )dλ } +
∫               A
needed to match light of that
wavelength alone. λ )
a(
{ b(λ )E(λ )dλ} +
∫              B

b(λ )                        { c(λ )E(λ )dλ}
∫              C
c(λ )
Not on a computer Screen
• These are color matching
functions

CSE190-B, Spring 2003                                                                    Computer Vision   CSE190-B, Spring 2003                                            Computer Vision
RGB Color Matching
CIE XYZ: Color
matching functions are
positive everywhere, but
primaries are imaginary.
Usually draw x, y, where
RGB: primaries are
x=X/(X+Y+Z)
monochromatic, energies are
y=Y/(X+Y+Z)
645.2nm, 526.3nm, 444.4nm.
Color matching functions have
negative parts -> some colors
can be matched only
subtractively.

CSE190-B, Spring 2003                                  Computer Vision   CSE190-B, Spring 2003                                              Computer Vision

CIE XYZ Color Matching Functions                                                         CIE -XYZ and x-y

CIE XYZ: Color
matching functions are
positive everywhere, but
primaries are imaginary.
Usually draw x, y, where
x=X/(X+Y+Z)
y=Y/(X+Y+Z)

CSE190-B, Spring 2003                                  Computer Vision   CSE190-B, Spring 2003                                              Computer Vision

CIE xyY (Chromaticity Space)                                                                 RGB to YIQ

The YIQ system is the colour primary system adopted by
NTSC for color television broadcasting. The YIQ color
solid is formed by a linear transformation of the RGB
cube. Its purpose is to exploit certain characteristics of the
human visual system to maximize the use of a fixed
bandwidth.

[Y]     [ 0.299 0.587 0.114 ] [ R ]
[ I ] = [ 0.596 -0.274 -0.322 ] [ G ]
[Q]     [ 0.212 -0.523 0.311 ] [ B ]

Note that “Y” captures intensity whereas I & Q capture the
effects of hue & saturation.

CSE190-B, Spring 2003                                  Computer Vision   CSE190-B, Spring 2003                                              Computer Vision
Metameric Lights
HSV Hexcone
(Metamers)
Hue, Saturation, Value
AKA: Hue, Saturatation, Intensity (HIS)

Hexagon arises from projection of cube onto plane
orthogonal to (R,G,B) = (1,1,1)
CSE190-B, Spring 2003                                             Computer Vision   CSE190-B, Spring 2003                               Computer Vision

Blob Tracking for Robot Control                                                                     Connected Regions

1 1 1
1 1 1         1 1 1   1
1 1 1       1 1       1
1 1   1 1       1 1
1 1 1     1 1 1   1
1

• What the connected regions in this binary image?
• Which regions are contained within which region?
CSE190-B, Spring 2003                                             Computer Vision   CSE190-B, Spring 2003                               Computer Vision

Connected Regions

1                     1 1 1 1 1
1 1                 1           1
1 1               1 1           1 1
1 1                     1 1 1 1 1

• What the connected regions in this binary image?
• Which regions are contained within which region?
CSE190-B, Spring 2003                                             Computer Vision

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