Image Processing 12-ColourImageProcessing.ppt

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Digital Image Processing

Colour Image Processing

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39Introduction

oda! we"ll loo# at colour image processing$co%ering:

& Colour fundamentals

& Colour models


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39Colour 'undamentals

In ())) *ir Isaac +ewton disco%ered thatwhen a beam of sunlight passes through a

glass prism$ the emerging beam is split into a

spectrum of colours

I m a

g e s t a # e n f r o m , o n - a l e - . W o o d s $

D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1


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39Colour 'undamentals cont1

he colours that humans and most animalspercei%e in an ob4ect are determined b! the

nature of the light reflected from the ob4ect

'or e5ample$ greenob4ects reflect light

with wa%e lengths

primaril! in the rangeof 600 & 670 nm while

absorbing most of the

energ! at other

wa%elengths

W h i t e 8i g h t

Colours

bsorbed

, r e e n 8 i g h t


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Chromatic light spans the electromagneticspectrum from appro5imatel! 00 to 700 nm

s we mentioned before human colour %ision

is achie%ed through ) to 7 million cones ineach e!e

I m a

g e s t a # e n f r o m , o n - a l e - . W o o d s $

D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1


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ppro5imatel! )) of these cones aresensiti%e to red light$ 33 to green light and

) to blue light

bsorption cur%es for the different cones ha%ebeen determined e5perimentall!

*trangel! these do not match the CI;

standards for red 700nm1$ green 6).(nm1and blue 36.


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39CI; Chromacit! Diagram

*pecif!ing colours s!stematicall! can beachie%ed using the CI; chromacity diagram

@n this diagram the 5Aa5is represents the

proportion of red and the !Aa5is represents theproportion of red used

he proportion of blue used in a colour is

calculated as: z = 1 – (x + y)


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39CI; Chromacit! Diagram cont1

I m a g e s t a # e n f r o m , o n - a l e - . W o o d s $

D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1 ,reen: )2 green$

26 red and (3blue

Bed: 32 green$)7 red and (blue


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39CI; Chromacit! Diagram cont1


D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1 his means the entire

colour range cannot be

displa!ed based on

an! three colours

he triangle shows the

t!pical colour gamut

produced b! B,

monitorshe strange shape is

the gamut achie%ed b!

high >ualit! colour

printers


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39Colour odels

'rom the pre%ious discussion it should beob%ious that there are different wa!s to model

colour

We will consider two %er! popular modelsused in colour image processing:

& B, Red Green Blue1

& EI* Hue Saturation Intensit!1


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39B,

In the B, model each colour appears in itsprimar! spectral components of red$ green and

blue

he model is based on a Cartesian coordinates!stem

& B, %alues are at 3 corners

& C!an magenta and !ellow are at three other corners

& lac# is at the origin

& White is the corner furthest from the origin

& Different colours are points on or inside the cube

represented b! B, %ectors


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39B, cont1


D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1


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39B, cont1

Images represented in the B, colour modelconsist of three component images & one for

each primar! colour

When fed into a monitor these images arecombined to create a composite colour image

he number of bits used to represent each

pi5el is referred to as the colour depth 2Abit image is often referred to as a fullA

colour image as it allows F ()$777$2()

colours

3

82( )


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39B, cont1


D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1


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39he E*I Colour odel

B, is useful for hardware implementationsand is serendipitousl! related to the wa! in

which the human %isual s!stem wor#s

Eowe%er$ B, is not a particularl! intuiti%ewa! in which to describe colours

Bather when people describe colours the!

tend to use hue$ saturation and brightnessB, is great for colour generation$ but E*I is

great for colour description


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39he E*I Colour odel cont1

he E*I model uses three measures todescribe colours:

& Hue: colour attribute that describes a pure

colour pure !ellow$ orange or red1 & Saturation: ,i%es a measure of how much a

pure colour is diluted with white light

& Intensity: rightness is nearl! impossible to

measure because it is so sub4ecti%e. Instead we

use intensit!. Intensit! is the same achromatic

notion that we ha%e seen in gre! le%el images


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39E*I$ Intensit! B,

Intensit! can be e5tracted from B, images &which is not surprising if we stop to thin#

about it

Bemember the diagonal on the B, colourcube that we saw pre%iousl! ran from blac# to

white

+ow consider if we stand this cube on theblac# %erte5 and position the white %erte5

directl! abo%e it


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39E*I$ Intensit! B, cont1


D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1 +ow the intensit! component

of an! colour can be

determined b! passing a

plane perpendicular tothe intenist! a5is and

containing the colour

point

he intersection of the plane

with the intensit! a5is gi%es us the intensit!

component of the colour


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39E*I$ Eue B,

In a similar wa! we can e5tract the hue fromthe B, colour cube

Consider a plane defined b!

the three points c!an$ blac#and white

ll points contained in

this plane must ha%e thesame hue c!an1 as blac#

and white cannot contribute

hue information to a colour I m a g e s t a # e n f r o m , o n - a l e - . W o o d s $

D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1


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39he E*I Colour odel


D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1 Consider if we loo# straight down at the B,

cube as it was arranged pre%iousl!

We would see a he5agonal

shape with each primar!colour separated b! (20G

and secondar! colours

at )0G from the primaries

*o the E*I model is

composed of a %ertical

intensit! a5is and the locus of colour points that

lie on planes perpendicular to that a5is


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D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1 o the right we see a he5agonal

shape and an arbitrar! colour

point

& he hue is determined b! anangle from a reference point$

usuall! red

& he saturation is the distance from the origin to the

point & he intensit! is determined b! how far up the

%ertical intenist! a5is this he5agonal plane sits not

apparent from this diagram


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I m

a g e s t a # e n f r o m , o n - a l e - . W o o d s $

D i g i t a l I m a

g e P r o c e s s i n g / 2 0 0 2 1 ecause the onl! important things are the angle

and the length of the saturation %ector this planeis also often represented as a circle or a triangle


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39E*I odel ;5amples

I m


D i g i t a l I m a g e P r o c e s s i n g / 2 0 0 2 1


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39E*I odel ;5amples

I m




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2<

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39Con%erting 'rom B, o E*I

,i%en a colour as B$ ,$ and its E$ *$ and I%alues are calculated as follows:

H =θ if B ≤G

360 −θ if B >G

⎨ ⎩

θ = cos−1

1

2 R −G( ) + R − B( )[ ]

R −G( )2+ R − B( ) G − B( )[ ]

1

2

⎨ ⎪

⎩ ⎪

⎪

⎭⎪

S =1−3

R +G + B( )

min R,G, B( )[ ]

I = 13 R +G + B( )


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39Con%erting 'rom E*I o B,

,i%en a colour as E$ *$ and I it"s B$ ,$ and %alues are calculated as follows:

& B, sector 0


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39Con%erting 'rom E*I o B, cont1

& B sector 240°


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39E*I B,

I m



B, Colour Cube

E$ *$ and I Components of B, Colour Cube


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39anipulating Images In he E*I odel

In order to manipulate an image under the EI*model we:

& 'irst con%ert it from B, to EI*

& Perform our manipulations under E*I & 'inall! con%ert the image bac# from E*I to B,

B,Image E*I Image B,Image

anipulations


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39B, AH E*I AH B,

I m

a g e s t a # e n f r o m , o n - a

l e - . W o o d s $


B,

Image

*aturation

Eue

Intensit!


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39Pseudocolour Image Processing

Pseudocolour also called falsecolour1 image processing consists

of assigning colours to gre! %alues

based on a specific criterionhe principle use of pseudocolour

image processing is for human

%isualisation & Eumans can discern betweenthousands of colour shades and

intensities$ compared to onl! about

two do-en or so shades of gre!

3) Pseudo Colour Image Processing


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Pseudo Colour Image Processing &

Intensit! *licing

Intensit! slicing and colour coding is one of thesimplest #inds of pseudocolour image

processing

'irst we consider an image as a 3D functionmapping spatial coordinates to intensities that

we can consider heights1

+ow consider placing planes at certain le%els

parallel to the coordinate plane

If a %alue is one side of such a plane it is

rendered in one colour$ and a different colour if

on the other side


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3< Pseudocolour Image Processing


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Pseudocolour Image Processing &

Intensit! *licing cont1

In general intensit! slicing can be summarisedas:

& 8et 0, L-1J represent the gre! scale

& 8et l0 represent blac# f(x, y) = 0J and let l L-1 represent white f(x, y) = L-1J

& *uppose P planes perpendicular to the intensit!

a5is are defined at le%els l 1, l 2, …, l p

& ssuming that 0 < P < L-1 then the P planes

partition the gre! scale into P + 1 inter%als V 1 , V 2 ,

…,V P+1

39 Pseudocolour Image Processing


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Pseudocolour Image Processing &

Intensit! *licing cont1

& ,re! le%el colour assignments can then be madeaccording to the relation:

& where ck is the colour associated with the k th

intensit! le%el V k defined b! the partitioning

planes at l = k – 1 and l = k

f ( x, y) = ck if f ( x, y)∈V k

0


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39B, AH E*I AH B, cont1

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39Chapter 6

Color Image Processing

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Image Processing 12-ColourImageProcessing.ppt

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Transcript of Image Processing 12-ColourImageProcessing.ppt