10Color Image Processing.ppt

8/10/2019 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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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

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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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*igitalImageProcessing(7==7


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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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*igitalImageProcessing(7==7 1ecause the only important things are the angle

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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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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.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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Im


*igitalIm

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Im


*igitalIm

ageProcessing(7==7


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Im


*igitalIm

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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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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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10Color Image Processing.ppt