10Color Image Processing.ppt
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Transcript of 10Color Image Processing.ppt
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Color ImageProcessing
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Color Fundamentals
Color fundamentals and models
Color transformations
Smoothing and sharpeningColor segmentation
PseudocolorSlicing
False-color maps
Index color
Multispectral color models
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Retinal Physiology and Color
Human retinas have (at least four types ofphotoreceptors!hree types of "cones#High light level$ high acuity vision
%ach type of cone has a different spectral response&ne type of "rods#'o-light level and peripheral vision
!here is su)stantive genetic diversity in color
receptors*ifferent spectral response of photoreceptor+)sence of one of the pigmentsMany more phenomena,,,
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Color Fundamentals
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Color Fundamentals
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Color Fundamentals
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Color Fundamentals
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Spectral Response of Cones
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Color Fundamentals
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Color Fundamentals
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CI% CHR&M+!ICI! *I+.R+M
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!ristimulus value
!he amounts of red$ green$ and )lue needed to form
any particular color are called the tristimulus values$denoted )y /$ $ and 0,
!richromatic coefficients
&nly to chromaticity coefficients are necessary to
specify the chrominance of a light,
ZYX
Zz
ZYX
Yy
ZYX
Xx
++
=
++
=
++
= ,,
1=++ ZYX
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CI% CHR&M+!ICI! *I+.R+M
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CI% CHR&M+!ICI! *I+.R+M
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C&'&R M&*%'S
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R.1 color Model
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R.1 color Model
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R.1In the R.1 model each colour appears in its
primary spectral components of red$ green and)lue
!he model is )ased on a Cartesian coordinate
systemR.1 values are at 2 corners
Cyan magenta and yello are at three other corners
1lac3 is at the origin
4hite is the corner furthest from the origin
*ifferent colours are points on or inside the cu)e
represented )y R.1 vectors
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R.1 color Model
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R.1 Color model
26
Active displays, such as computer monitors and television sets, emit
combinations of red, green and blue light. This is an additivecolor model
Source: www.mitsubishi.com
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R.1 (cont5Images represented in the R.1 colour model
consist of three component images 6 one for
each primary colour
4hen fed into a monitor these images are
com)ined to create a composite colour image
!he num)er of )its used to represent each pixel
is referred to as the colour depth
+ 78-)it image is often referred to as a full-colour image as it allos 9 :;$
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R.1 color Model
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R.1 %xample
!
"riginal #reen $and $lue $and%ed $and
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CM color model
If the intensities are represented as =>r$g$)>: and
=>c$m$y>: (also coordinates =-7?? can )e
used$ then the relation )eteen R.1 and CM
can )e descri)ed as@
c
m
y
=
1
1
1
r
g
b
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CM Color model
2
&assive displays, such as color in'(et printers, absorblight instead of
emitting it. )ombinations of cyan, magentaand yellowin's are used. This
is a subtractivecolor model.
Source: www.hp.com
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CMA model
For printing and graphics art industry$ CM
is not enoughB a fourth primary$ A hich
stands for )lac3$ is added,
Conversions )eteen R.1 and CMA are
possi)le$ although they reuire some extra
processing,
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HIS color model
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HSI$ Intensity G R.1
Intensity can )e extracted from R.1 images 6
hich is not surprising if e stop to thin3
a)out it
Remem)er the diagonal on the R.1 colourcu)e that e sa previously ran from )lac3 to
hite
o consider if e stand this cu)e on the)lac3 vertex and position the hite vertex
directly a)ove it
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HSI$ Hue G R.1
In a similar ay e can extract the hue from the
R.1 colour cu)e
Consider a plane defined )y
the three points cyan$ )lac3and hite
+ll points contained in
this plane must have the
same hue (cyan as )lac3
and hite cannot contri)ute
hue information to a colour
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!he HSI Colour Model (cont5
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*igitalImageProcessing(7==7 !o the right e see a hexagonal
shape and an ar)itrary colour
point
!he hue is determined )y anangle from a reference point$
usually red
!he saturation is the distance from the origin to the
point!he intensity is determined )y ho far up the vertical
intenisty axis this hexagonal plane sits (not apparent
from this diagram
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!he HSI Colour Model (cont5
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and the length of the saturation vector this planeis also often represented as a circle or a triangle
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HSI Model %xamples
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HSI Model %xamples
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Converting colors from R.1 to HSI
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Converting from HSI to R.1
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Manipulating Images In !he HSI
Model
In order to manipulate an image under the
HIS model e@First convert it from R.1 to HIS
Perform our manipulations under HSI
Finally convert the image )ac3 from HSI to
R.1R.1
Image HSI ImageR.1
Image
Manipulations
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Pseudocolour Image Processing
Pseudocolour (also called falsecolour image processing consists
of assigning colours to grey values
)ased on a specific criterion!he principle use of pseudocolour
image processing is for human
visualisationHumans can discern )eteenthousands of colour shades and
intensities$ compared to only a)out
to doen or so shades of grey
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Pseudo Colour Image Processing 6
Intensity Slicing
Intensity slicing and colour coding is one of thesimplest 3inds of pseudocolour image processing
First e consider an image as a 2* function
mapping spatial coordinates to intensities (that
e can consider heights
o consider placing planes at certain levels
parallel to the coordinate plane
If a value is one side of such a plane it isrendered in one colour$ and a different colour if
on the other side
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Pseudocolour Image Processing 6
Intensity Slicing (cont5
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Pseudocolour Image Processing 6
Intensity Slicing (cont5
In general intensity slicing can )e summarised
as@'et J0, L-1K represent the grey scale
'et l=represent )lac3 Jf(x, y) = 0K and let lL-1represent hite Jf(x, y) = L-1K
SupposePplanes perpendicular to the intensity
axis are defined at levels l1,l2, , lp
+ssuming that 0 < P < L-1 then thePplanes
partition the grey scale intoP + 1 intervals V1, V2,
,VP+1
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Pseudocolour Image Processing 6
Intensity Slicing (cont5
.rey level colour assignments can then )e
made according to the relation@
here ckis the colour associated ith the kthintensity level Vkdefined )y the partitioning
planes at l = k 1andl = k
f(x,y) = ck iff(x,y) Vk
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R.1 -L HSI -L R.1 (cont5
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R.1 -L HSI -L R.1 (cont5
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R.1 -L HSI -L R.1 (cont5
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R.1 -L HSI -L R.1 (cont5
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C l ! f ti
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Color !ransformations
Formulation
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!one and Color Corrections
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!one and Color Corrections
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!one and Color Corrections
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Histogram Processing
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Color Image Sharpening
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Color Segmentation
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Segmentation
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Segmentation
in R.1 ector Space
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Color %dge *etection
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