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7/30/2019 Boje i Svetlost Spektri Fundamentals
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Color Image Processing
CS555 Digital Image Processing
Dr. Amar Raheja
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Elements of Colour
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Color Fundamentals
Digital Image Processing 3
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Color Fundamentals
Digital Image Processing 4
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Color Fundamentals
6 to 7 million cones in the human eye can be divided intothree principal sensing categories, corresponding roughlyto red, green, and blue.
65%: red 33%: green 2%: blue (blue cones are themost sensitive)
Digital Image Processing 5
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Color Fundamentals
Digital Image Processing 6
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How we see color
It all depends on how much the differentcones are stimulated
It is possible to have two different spectrathat stimulate cones the same wayoCalled a metamer
To a person, these colors look the same, butthey are (in some sense) completelydifferent
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Some colors do not come from a
single wavelengthThere will never be a purple laserPurple comes from blue (short wavelength)
and red (long wavelength) lightoMore precisely, the sensation that we call purple
comes from the blue and red cones beingstimulated
And no others!
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9
Human Eye The photosensitive part
of the eye is called theretina.
The retina is largelycomposed of two typesof cells, called rodsandcones. Only the conesare responsible for colorperception.
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Cones Cones are most densely packed
within a region of the eye calledthe fovea
There are three types of cones,referred to as S, M, and L. Theyare roughly equivalent to blue,
green, and red sensors,respectively.o Their peak sensitivities are
located at approximately430nm, 560nm, and 610nm forthe "average" observer.
Spectrum is encoded into threevalues that correspond to eachtype of cone - trichromacy
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Non-uniform distribution
Blue cones are least dense in the foveao3-5%, versus about 8% elsewhere
Red cones are about 33%, fairly evenlydistributed
Green are 64% in the fovea, about 55%elsewhere
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Another way to see this
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Color constancy
As the spectrum of the illuminating lightchanges, so does the pattern of cone
stimulusoYet your red coat looks the same as you walk
outside!
oNo one has a good (computational)understanding of this problem
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How many colors can we see?
Humans can discriminate abouto200 hueso
20 saturation valueso500 brightness steps
The NBS lists 267 color namesWhat about across languages?
oSeem to be about 11 basic ones white, black, red, green, yellow, blue, brown, purple,
pink, orange, gray
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Additive versus subtractive
colorsPaint is colored because of the spectrum it
absorbs(subtracts from the incident light)
oRed paint absorbs non-red photons
oColor filters are another exampleLights have colors because of the spectrum
they emit
oTelevisions and monitors work this wayThe two obey different rules!
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Subtractive colors
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Additive colors
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Cheap versus expensive
camerasCheap color (video) cameras have a single
CCD
oMask in front of the imaging array
oReduces spatial resolutionMore expensive cameras have 3 different
video cameras
oColor output really is 3 different (independent)signals
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Digital Image Processing 19
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Colorin Cameras, Scanners and Monitors isgenerated from 3 primary colors - Red, Green and
Blue
NOTE: The 3 sensors generate 3 monochromeimages (the coloriscreated in the brain)
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In printers, the inks subtractthe lightusing three Subtractive primaries
From the graph above:o Cyan = Green + Blue = -Redo Magenta = Red + Blue = -
Green
oYellow = Red + Green = -Blueo BlacK = - (Red+Blue+Green)
= - White = BLACK
Red
Green Blue
MagentaYellow
Cyan
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Color Spacesare universally agreed
upon descriptions of color
Device
Dependent
RGBCMYK
Device
Independent
XYZCIE L*a*b*
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X and Y and Z (pronounced Cap-
X, Cap-Y, Cap-Z)
TRISTIMULUS values (XYZ) define color numerically
X = S * R *x-barY = S * R * y-bar
Z = S * R * z-bar
Source S()
Reflector R()
Color Matching Functions x-bar, ybar, zbar
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CIE Chromaticity diagramshows all the
colors we see (color gamut of eye)
Represents the XYZ color space Problem: Perceptually NON-UNIFORM
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CIEL*a*b* was motivated by a need for aperceptually uniformcolor space
L* = Lightness (Luminance)(0-100) a* = colors (Chrominance1) from Red to Green b*= (Chrominance2) colors from blue to yellow
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Color Spaces
+source (white point)
+x-bar, y-bar, z-bar
R
G
B
X
Y
Z
+math
(matrix
algebra)
CIE Lab
Device dependent Device In-dependent
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What do we know so far
Coloris a combination of Source, Subjectand Detector
Color Spacesare universally agreedupon, numerical representations of color
o Rgb AND Cmyk are device dependentcolorspaces
o RGB + source + eye = XYZ (a deviceindependent color space)
o XYZ + perceptual uniformity=LabLets manage color !!
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Representing Colour
How can a particular colour be precisely andunambiguously described?
Verbal descriptions such as Dark blue,Bright red, Slimy green are too broad
Description of its spectral density curve, byspecifying its level at a number of
wavelengths is awkward, and too specific,as many different spectral shapes producethe same perceived colour
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Numeric Colour Description
Ideally, every colour should be described uniquelyin some numeric way
How many numbers are required to define acolour?
What coding scheme can be used to map coloursinto numbers, and vice versa?
There are several different conventions for codingcolours, what are they, and how do they relate toeach other?
International standard for colour description?
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Color Fundamentals
The characteristics generally used to distinguish one colorfrom another are brightness, hue, and saturation
brightness: the achromatic notion of intensity.
hue: dominant wavelength in a mixture of light waves,represents dominant color as perceived by an observer.
saturation: relative purity or the amount of white lightmixed with its hue.
Digital Image Processing 30
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Dominant Wavelength Theory
Capitalizes on the variety of spectra that producethe same perceived colour
Specifies a spectrum having this simple shape:
400 700
A
D
B
620
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Dominant Wavelength Theory Luminance is the total power in the light:
o L = (D - A)B + AW (W = ?) Hue is the location of the dominant wavelength, i.e. the
colour of the main pure light present (in previous e.g. its
red)
Saturation is the purity of the light, i.e. the percentage ofluminance that resides in the dominant component:
oS =
(D - A)B
LX 100%
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Dominant Wavelength cont.
The dominant wavelength, luminance andsaturation fully define a colour
When D = A, saturation is 0, and white light isseen. When A=0, a pure light is seen. Pastelcolours contain much white light, and aretherefore unsaturated.
The eye can distinguish about 200 different hues,and about 20 different saturations (depending onthe hue).
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3-dimensional colour spaces
Saturation, luminance and hue are useful conceptsfor describing colour
However, not very easy to measure these valueswhen presented with a sample colour
It does, however, illustrate the fact that colourperception is three-dimensional, i.e. that anycolour may be described uniquely by exactly three
numbers
Any colour can be represented as a point in athree-dimensional colour space.
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CIE Color Space
In order to achieve a representation which uses only positive mixing coefficients, the CIE("Commission Internationale d'Eclairage in 1931) defined three new hypothetical lightsources, x, y, and z, which yield positive matching curves:
If we are given a spectrum and wish to find the corresponding X, Y, and Z quantities, wecan do so by integrating the product of the spectral power and each of the threematching curves over all wavelengths. The weights X,Y,Z form the three-dimensional CIEXYZ space, as shown above.
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CIE Standard
Standard based on three primaries which are able toproduce ALL visible colours.
Often it is convenient to work in a 2D color space This is commonly done by projecting the 3D color space
onto the plane X+Y+Z=1, yielding a CIE chromaticitydiagram
CIE chromaticity diagram is the view you would get lookingat the plane X+Y+Z=1, straight down the blue axis
Provides a standard reference for comparing other colorsystems
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CIE Chromticity Diagram Less natural than RGB However this standard is
useful for converting betwencolour spaces of differentdevices
Projections defined as :
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CIE Chromaticity Diagram complementary colors
colors which can be mixedtogether to yield white light.For example, colors onsegment CD arecomplementary to the colorson segment CB.
dominant wavelength The spectral color which can
be mixed with white light inorder to reproduce thedesired color. color B in theabove figure is the dominantwavelength for color A.
non-spectral colors colors not having a dominant
wavelength. For example,color E in the above figure.
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Color Gamuts Color gamuts are represented
on the chromaticity diagram as
straight line segments orpolygons
Three primaries (from thevertices of the orange triangle)
can only generate colors on theedges or inside the bounding
edges of the triangle.
Hence, no set of 3 primaries canbe additively combined to
generate all perceived colors
o Because no triangle within thediagram can encompass all
colors
Color gamuts traditionally usedto compare video monitors andhard-copy devices
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Tri-stimulus theory
Any colour can be constructed as a linear combination ofthree primary colours, e.g.
C = n1R + n2G + n3B (n1, n2, n3 scalars) (doesnt have to be red, green and blue, can be any three
primaries)
e.g. RGB(0,1,0) would be pure green, CMY(.2,.3,.5) wouldbe a yellow
Problem! To produce all perceivable colours, some of theabove scalars must be negative. This makes no physicalsense. Light cannot be removed that isnt there.
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The RGB Colour Cube On a display with 3 colour phosphors/lamps/LEDs, the possible
magnitudes of each colour vary from 0 to 1.
Thus the space of possible colours in R, G, B space is a unit cube The RGB colour cube is a well known vector space defining all possible
colour combinations based on the RGB basis vectors
E.g. (0, 0, 0) Black, (1, 0, 0) Red, (0, 1, 0) Green, (0, 0, 1) Blue, (1,1, 0) Yellow, (1, 0, 1) Magenta, (0, 1, 1) Cyan, (1, 1, 1) White
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CIE Chromaticity Diagram
It shows colorcompositionas a functionof x (red) andy (green)
Digital Image Processing 42
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RGB Color Model
Digital Image Processing 43
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RGB Color Model
Digital Image Processing 44
Pixel depth
The total number ofcolors in a 24-bit RGB
image is (28)3 =16,777,216
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Digital Image Processing 45
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Digital Image Processing 46
Safe RGB colors (orsafe Web colors) are
reproduced faithfully,reasonably
independently ofviewer hardware
capabilities
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Digital Image Processing 47
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The CMY and CMYK Color Models
Digital Image Processing 48
1
1
1
C R
M G
Y B
=
Equal amounts of the pigment primaries, cyan, magenta, andyellow should produce black. In practice, combining thesecolors for printing produces a muddy-looking black.
To produce true black, the predominant color in printing, thefourth color, black, is added, giving rise to the CMYK colormodel.
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Digital Image Processing 49
http://en.wikipedia.org/wiki/CMYK
CMY vs. CMYK
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HSI Color Model
Digital Image Processing 50
brightness: the achromatic notion ofintensity.
hue: dominant wavelength in a mixture of light waves, represents dominant color
as perceived by an observer.
saturation: relative purity or the amount of white light mixed with its hue.
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HSI Color Model
Digital Image Processing 51
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HSI Color Model
Digital Image Processing 52
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HSI Color Model
Digital Image Processing 53
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Converting Colors from RGB to HSI
Given an image in RGB color format, the H component ofeach RGB pixel is obtained using the equation
Digital Image Processing 54
if B G
360 if B>GH
=
[ ]
( )
1
1/22
1( ) ( )
2
cos ( )( )
R G R B
R G R B G B
+
=
+
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Converting Colors from RGB to HSI
Given an image in RGB color format, the saturationcomponent is given by
Digital Image Processing 55
[ ]3
1 min( , , )( )S R G BR G B=
+ +
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Converting Colors from RGB to HSI
Given an image in RGB color format, the intensitycomponent is given by
Digital Image Processing 56
( )1
3I R G B= + +
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Converting Colors from HSI to RGB
RG sector
Digital Image Processing 57
(1 )
cos1
cos(60 )
and
3 ( )
B I S
S HR I
H
G I R B
=
= +
= +
o
(0 120 )H
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Converting Colors from HSI to RGB
RG sector
Digital Image Processing 58
120
(1 )
cos1
cos(60 )
and
3 ( )
H H
R I S
S HG I
H
B I R G
=
=
= +
= +
o
o
(120 240 )H
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Converting Colors from HSI to RGB
RG sector
Digital Image Processing 59
240
(1 )
cos1
cos(60 )
and
3 ( )
H H
G I S
S HB I
H
R I G B
=
=
= +
= +
o
o
(240 360 )H o o
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Digital Image Processing 60
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Digital Image Processing 61
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Digital Image Processing 62