L19-20_ColorImageProcessing

Lecture 19

Color Image Processing

Preview

• Why use color in image processing?

– Color is a powerful descriptor

• Object identification and extraction

• e.g., Face detection using skin colors

– Humans can discern thousands of color shades and intensities

• c.f. Human discern only two dozen shades of grays

Preview

• Two category of color image processing

– Full color processing

• Images are acquired from full-color sensor or equipments

– Pseudo-color processing

• In the past decade, color sensors and processing hardware are not available

• Colors are assigned to a range of monochrome intensities

Color fundamentals

• Physical phenomenon – Physical nature of color is known (1666, Isaac Newton)

• Chromatic light span the electromagnetic spectrum (EM) from 400

to 700 nm

• Physiopsychological phenomenon

– How human brain perceive and interpret color?

Color fundamentals

• The color that human perceive in an object = the light reflected from the object

Illumination source scene

reflection eye

Physical quantities to describe a chromatic light source

• Radiance: total amount of energy that flow from the light source, measured in watts (W)

• Luminance: amount of energy an observer perceives from a light source, measured in lumens (lm)

– Far infrared light: high radiance, but 0 luminance

• Brightness: subjective descriptor that is hard to measure, similar to the achromatic notion of intensity

How human eyes sense light?

• 6~7M Cones are the sensors in the eye

• 3 principal sensing categories in eyes

– Red light 65%, green light 33%, and blue light 2%

Primary and secondary colors

• In 1931, CIE(International Commission on Illumination) defines specific wavelength values to the primary colors

– B = 435.8 nm, G = 546.1 nm, R = 700 nm

– However, we know that no single color may be called red, green, or blue

• Secondary colors: G+B=Cyan, R+G=Yellow, R+B=Magenta

Application of additive nature of light colors

• Color TV

Application of additive nature of light colors

• Color TV

CIE XYZ model

• RGB -> CIE XYZ model

• Normalized tristimulus values

ZYX

Xx

ZYX

Yy

ZYX

Zz

B

G

R

Z

Y

X

939.0130.0020.0

071.0707.0222.0

178.0342.0431.0

=> x+y+z=1. Thus, x, y (chromaticity coordinate) is

enough to describe all colors

By additivity of colors:

Any color inside the

triangle can be produced

by combinations of the

three initial colors

RGB gamut of

monitors

Color gamut of

printers

Color models

• Color model, color space, color system – Specify colors in a standard way

– A coordinate system that each color is represented by a single point

• RGB model

• CYM model

• CYMK model

• HSI model

Suitable for hardware or

applications

- match the human description

RGB color model

Pixel depth

• Pixel depth: the number of bits used to represent each pixel in RGB space

• Full-color image: 24-bit RGB color image

– (R, G, B) = (8 bits, 8 bits, 8 bits)

Application of additive nature of light colors

• Color TV

Safe RGB colors

• Subset of colors is enough for some application

• Safe RGB colors (safe Web colors, safe browser colors)

(6)3 = 216

Safe RGB color

Safe RGB color

Safe color cube Full color cube

RGB color model

CMY model (+Black = CMYK)

• CMY: secondary colors of light, or primary colors of pigments

• Used to generate hardcopy output

B

G

R

Y

M

C

1

1

1

HSI color model

• Will you describe a color using its R, G, B components?

• Human describe a color by its hue, saturation, and brightness

– Hue: color attribute

– Saturation: purity of color (white->0, primary color->1)

– Brightness: achromatic notion of intensity

HSI color model

• These spaces use a cylindrical (3D-polar) coordinate system to encode the following three psycho-visual coordinates: – Hue (dominant colour seen)

• Wavelength of the pure colour observed in the signal.

• Distinguishes red, yellow, green, etc.

• More the 400 hues can be seen by the human eye.

– Saturation (degree of dilution)

• Inverse of the quantity of “white” present in the signal. A pure colour has 100% saturation, the white and grey have 0% saturation.

• Distinguishes red from pink, marine blue from royal blue, etc.

• About 20 saturation levels are visible per hue.

– Brightness

• Amount of light emitted.

• Distinguishes the grey levels.

• The human eye perceives about 100 levels.

HSI color model

• RGB -> HSI model

Intensity

line

saturation

Colors on this triangle

Have the same hue

HSI model: hue and saturation

HSI model

RGB to HSI

1

1/2

;

360 ;

1[( ) ( )]

2{ }2

31 [ ( , , )]

( )

( )

3

cos[ ( )( )]( )

B GH

B G

R G R B

S MIN R G BR G B

R G BI

R B G BR G

HSI to RGB

(1 )

cos[1 ]

cos(60 )

3 ( )

B I S

S HR I

H

G I R B

RG Sector

(0H<120):

120

(1 )

cos[1 ]

cos(60 )

3 ( )

H H

R I S

S HG I

H

B I R G

GB Sector

(120H<240):

240

(1 )

cos[1 ]

cos(60 )

3 ( )

H H

G I S

S HB I

H

R I B G

BR Sector

(240H<360):

HSI component images

R,G,B Hue

saturation intensity

Example 1

Color Image

Hue Saturation Luminance

Example 2

Color Image

Hue Saturation Luminance

Color spaces

• RGB (CIE), RnGnBn (TV - National Television Standard Comittee) • XYZ (CIE) • UVW (UCS de la CIE), U*V*W* (UCS modified by the CIE) • YUV, YIQ, YCbCr • YDbDr • DSH, HSV, HLS, IHS • Munsel colour space (cylindrical representation) • CIELuv • CIELab • SMPTE-C RGB • YES (Xerox) • Kodak Photo CD, YCC, YPbPr, ...

HSV

IHS

HSI

HLS

triangle • Yet there are many such spaces

described in books.

• How does one choose which one

to use?

Lecture 20

Color Image Processing

Pseudo-color image processing

• Assign colors to gray values based on a specified criterion

• For human visualization and interpretation of gray-scale events

• Intensity slicing

• Gray level to color transformations

Intensity slicing

• 3-D view of intensity image

Image plane

Color 1

Color 2

Intensity slicing

• Alternative representation of intensity slicing

Application 1

X-ray image of a weld

Intensity slicing

• More slicing plane, more colors

Application 2

8 color regions Radiation test pattern

* See the gradual gray-level changes

Gray level to color transformation

Application 1

Combine several monochrome images

Example: multi-spectral images

Rainfall statistics

R G

B

Near

Infrared

(sensitive

to biomass)

R+G+B near-infrared+G+B

Washington D.C.

Color pixel

• A pixel at (x,y) is a vector in the color space

– RGB color space

),(

),(

),(

),(

yxB

yxG

yxR

yxc

c.f. gray-scale image

f(x,y) = I(x,y)

Example: spatial mask

How to deal with color vector?

• Per-color-component processing

– Process each color component

• Vector-based processing

– Process the color vector of each pixel

• When can the above methods be equivalent?

– Process can be applied to both scalars and vectors

– Operation on each component of a vector must be independent of the other component

Two spatial processing categories

• Similar to gray scale processing studied before, we have to major categories

• Pixel-wise processing

• Neighborhood processing

Color transformation

• Similar to gray scale transformation

– g(x,y)=T[f(x,y)]

• Color transformation

nirrrTs nii ,...,2,1 , ),...,,( 21

g(x,y) f(x,y)

s1

s2

…

sn

f1

f2

…

fn

T1

T2

…

Tn

Use which color model in color transformation?

• RGB CMY(K) HSI

• Theoretically, any transformation can be performed in any color model

• Practically, some operations are better suited to specific color model

Example: modify intensity of a color image

• Example: g(x,y)=k f(x,y), 0<k<1 • HSI color space

– Intensity: s3 = k r3

– Note: transform to HSI requires complex operations

• RGB color space – For each R,G,B component: si = k ri

• CMY color space – For each C,M,Y component: – si = k ri +(1-k)

I H,S

Implementation of color slicing

• Recall the pseudo-color intensity slicing

1-D intensity

Implementation of color slicing

• How to take a region of colors of interest?

prototype color

Sphere region

prototype color

Cube region

Application

cube sphere

Color image smoothing

• Neighborhood processing

Color image smoothing: averaging mask

xySyx

yxK

yx),(

),(1

),( cc

Neighborhood

Centered at (x,y)

xy

xy

xy

Syx

Syx

Syx

yxBK

yxGK

yxRK

yx

),(

),(

),(

),(1

),(1

),(1

),(c

vector processing

per-component processing

original R

G B

H S

I

Example: 5x5 smoothing mask

RGB model

Smooth I

in HSI model difference

Example: Image Sharpening

Lighting conditions

• The lighting conditions of the scene have a large effect on the colours recorded.

Image taken lit by a flash. Image taken lit by a

tungsten lamp.

Lighting conditions

• The following four images of the same scene were acquired under different lighting conditions.

Dealing with Lighting Changes

• Knowing just the RGB values is not enough to know everything about the image. – The R, G and B primaries used by different devices are usually

different.

• For scientific work, the camera and lighting should be calibrated.

• For multimedia applications, this is more difficult to organise: – Algorithms exist for estimating the illumination colour.