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Color Image

Processing

4.6 Color Image Processing

• Color

– simplifies object extraction and identification

– human vision : thousands of colors vs max-24

gray levels

• Color Spectrum

– white light with a prism (1966, Newton)

4.6 Color Image Processing

• RGB : Color Monitor, Color Camera, Color Scanner

• CMY : Color Printer, Color Copier

• YIQ : Color TV 표준, Y(luminance), I(Inphase),

Q(quadrature)

– HSI, HSV

4.6 Color Image Processing

• RGB Model

4.6 Color Image Processing

• CMY Model

– Color Printer, Color Copier

– RGB data CMY

−

=

B

G

R

Y

M

C

1

1

1

4.6 Color Image Processing

×

−

−−=

B

G

R

Q

I

Y

311.0523.0212.0

321.0275.0596.0

114.0587.0299.0

×

−

−−=

Q

I

Y

B

G

R

705.1108.11

647.0272.01

620.0956.01

4.6 Color Image Processing

4.6 Color Image Processing

• RGB to HSI Conversion

1,,,0 where),(3

1≤≤++= BGRIBGRI

002

1 if },))(()(

)]()[(2

1

{cos bgBGBRGR

BRGRH >

−−+−

−+−= −

00 if ,360 bgHH <−= o

}),,(min{3

1 BGRBGR

S ×++

−=

IBbIGg / ,/ where 00 ==

4.6 Color Image Processing

• HSI to RGB Conversion

BRG

H

HSR

SB

−−=

−+=

−=

1

])60cos(

cos1[

3

1

)1(3

1

o

oo 1200 assume ≤≤ H

Image Retrieval Application

• Content-Based Image Retrieval System

Index

Retrieval

Query

featurefeaturefeaturefeature extraction extraction extraction extraction

Image databaseImage databaseImage databaseImage database

compression imagecompression imagecompression imagecompression image

indexindexindexindexfeature informationfeature informationfeature informationfeature information

decompressiondecompressiondecompressiondecompression

resultresultresultresult

queryqueryqueryquery

Image Retrieval Application

• Color Features for Image Indexing

– Color Histogram

• an estimate of the probability of occurrence of color

intensities

• 장점 : simple and geometric invariance(translation,

rotation, and scaling)

• 단점 : lack of spatial information of objects

– Dominant Colors

• image의 대표 색상

• 잡음에 무관

– Color Monments

Image Retrieval Application

• Example of Color Histogram in HSI Model

- Hue : range [0, 360]

- Saturation : range[0, 1]

- Intensity : range[0, 1]

- Total 36 bin quantization

Hue : 6bin

Saturation : 2bin

Intensity: 3bin

• Image representation

• Image statistics

• Histograms (frequency)

• Entropy (information)

• Filters (low, high, edge, smooth)

The Course

• Books

– Computer Vision – Adrian

Lowe

– Digital Image Processing –

Gonzalez, Woods

– Image Processing, Analysis

and Machine Vision – Milan

Sonka, Roger Boyle

Introduction to Digital Image

Processing

• Human vision - perceive and understand world

• Computer vision, Image Understanding / Interpretation,

Image processing.

– 3D world -> sensors (TV cameras) -> 2D images

– Dimension reduction -> loss of information

• low level image processing

• transform of one image to another

• high level image understanding• knowledge based - imitate human cognition

• make decisions according to information in image

Introduction to Digital Image

Processing

HIGH

MEDIUM

LOW

Algorithm Complexity Increases

Classification / decision

Raw data

Amount of Data Decreases

• Acquisition,

preprocessing

– no intelligence

• Extraction, edge

joining

• Recognition,

interpretation

– intelligent

Low level digital image

processing• Low level computer vision ~ digital image processing

• Image Acquisition

– image captured by a sensor (TV camera) and digitized

• Preprocessing

– suppresses noise (image pre-processing)

– enhances some object features - relevant to understanding the image

– edge extraction, smoothing, thresholding etc.

• Image segmentation

– separate objects from the image background

– colour segmentation, region growing, edge linking etc

• Object description and classification

– after segmentation

Signals and Functions

• What is an image

• Signal = function (variable with physical meaning)

– one-dimensional (e.g. dependent on time)

– two-dimensional (e.g. images dependent on two co-ordinates

in a plane)

– three-dimensional (e.g. describing an object in space)

– higher-dimensional

• Scalar functions

– sufficient to describe a monochromatic image - intensity

images

• Vector functions

– represent color images - three component colors

Image Functions

• Image - continuous function of a number of variables

• Co-ordinates x, y in a spatial plane

– for image sequences - variable (time) t

• Image function value = brightness at image points

– other physical quantities

– temperature, pressure distribution, distance from the observer

• Image on the human eye retina / TV camera sensor - intrinsically

2D

• 2D image using brightness points = intensity image

• Mapping 3D real world -> 2D image

– 2D intensity image = perspective projection of the 3D scene

– information lost - transformation is not one-to-one

– geometric problem - information recovery

– understanding brightness info

Image Acquisition & Manipulation

• Analogue camera

– frame grabber

– video capture card

• Digital camera / video recorder

• Capture rate � 30 frames / second

– HVS persistence of vision

• Computer, digitised image, software

(usually c)

• f(x,y) � #define M 128

#define N 128

unsigned char f[N][M]

• 2D array of size N*M

• Each element contains an intensity value

Image definition

• Image definition:

– A 2D function obtained by sensing a scene

– F(x,y), F(x1,x2), F(x)

– F- intensity, grey level

– x,y - spatial co-ordinates

• No. of grey levels, L = 2B

• B = no. of bits

B L Description

1 2 Binary Image (black and white)

6 54 64 levels, limit of human visual system

8 256 Typical grey level resolution

f(N-1,M-1)

f(o,o)

N

M

Brightness and 2D images

• Brightness dependent several factors

– object surface reflectance properties

• surface material, microstructure and marking

– illumination properties

– object surface orientation with respect to a viewer and light source

• Some Scientific / technical disciplines work with 2D images

directly

– image of flat specimen viewed by a microscope with transparent

illumination

– character drawn on a sheet of paper

– image of a fingerprint

Monochromatic images

• Image processing - static images - time t is constant

• Monochromatic static image - continuous image

function f(x,y)

– arguments - two co-ordinates (x,y)

• Digital image functions - represented by matrices

– co-ordinates = integer numbers

– Cartesian (horizontal x axis, vertical y axis)

– OR (row, column) matrices

• Monochromatic image function range

– lowest value - black

– highest value - white

• Limited brightness values = gray levels

Chromatic images

• Colour

– Represented by vector not scalar

• Red, Green, Blue (RGB)

• Hue, Saturation, Value (HSV)

• luminance, chrominance (Yuv , Luv)

Red

Green

Hue degrees:

Red, 0 deg

Green 120 deg

Blue 240 deg

Green

V=0

S=0

Use of colour space

Image quality

• Quality of digital image proportional to:

– spatial resolution

• proximity of image samples in image plane

– spectral resolution

• bandwidth of light frequencies captured by sensor

– radiometric resolution

• number of distinguishable gray levels

– time resolution

• interval between time samples at which images captured

Image summary

• F(xi,yj)

• i = 0 --> N-1

• j = 0 --> M-1

• N*M = spatial resolution, size of

image

• L = intensity levels, grey levels

• B = no. of bits

f(N-1,M-1)

f(o,o)

N

M

Digital Image Storage

• Stored in two parts

– header

• width, height … cookie.

– Cookie is an indicator of what type of image file

– data

• uncompressed, compressed, ascii, binary.

• File types

– JPEG, BMP, PPM.

PPM, Portable Pixel Map

• Cookie

– Px

• Where x is:

• 1 - (ascii) binary image (black & white, 0 & 1)

• 2 - (ascii) grey-scale image (monochromic)

• 3 - (ascii) colour (RGB)

• 4 - (binary) binary image

• 5 - (binary) grey-scale image (monochromatic)

• 6 - (binary) colour (RGB)

PPM example

• PPM colour file RGB

P3

# feep.ppm

4 4

15

0 0 0 0 0 0 0 0 0 15 0 15

0 0 0 0 15 7 0 0 0 0 0 0

0 0 0 0 0 0 0 15 7 0 0 0

15 0 15 0 0 0 0 0 0 0 0 0

Tools

Installing CVIPtools• Download the appropriate installation from

http://www.ee.siue.edu/CVIPtools/

• Double-click on binWin32.tar.gz

• Agree to the WinZip conditions.

• Answer the “Should WinZip decompress it to a temporary

folder and open it?” YES

• You will see the WinZip window with over 1100 files, find

the file install.exe and double-click on it

• You will see an “Install” window with the statement

“WinZip will extract all files to a temporary folder and run

the install.exe program” Press OK

Installing CVIPtools• WinZip will extract the files to c:\windows\TEMP\install

• You will see the CVIPtools Installation window with the

statement “Press OK to proceed with installation, Press

Cancel to abort installation” Press OK

• You will see the Select source drive directory, which

should be C:\WINDOWS\TEMP\install. Press OK

• You will see the Select destination drive and directory.

Press the “..” until you see the directory C:\CVIPtools,

then press OK

• You will see the Select installation type and options

window. Select Binary installation and press OK

Installing CVIPtools• You will see a Copying files message. Wait for the

program to finish. You will see the CVIPtools Installation

complete message. Press OK.

• You will see a Install window with the question. When

install.exe completes press the OK button to return to

WinZip. Press OK.

• Exit from WinZip

• Go to C:\CVIPtools\bin

• Make a shortcut to CVIPtools.bat

• To run CVIPtools double-click on the shortcut.

Image presentation (1)

1.1 Image capture,

representation,

and storage:

digital image, DPI,

pixel...

Example: Various

quantizing level:

(a) 6 bits; (b) 4

bits; (c) 2 bits; (d)

1 bit.

Image presentation (2)

• 1.2 Color representation:

Color systems: RGB, CMY/CMYK, HSI, YCbCr

Sources

• Department of Pattern Recognition and

Knowledge Engineering

• Institute of Information Technology

• Hanoi, Vietnam

• Represented by LUONG CHI MAI

• lcmai@ioit.ncst.ac.vnEinführung in die

erweiterte Realität Virtual Reality Modeling

Language (VRML) Prof. G. Klinker, Prof.

B. Brügge 19. Mai 2000

• Schenney UC Berkeley

SUPPLEMENTAL READING

�Jensen. 1996. Introductory Digital Image

Processing. (Upper Saddle River, NJ: Prentice

Hall). Ed. 2. Ch. 2 (60-61), Ch. 3, and Ch. 4

Sources

• Maja Mataric

• Dodds, Harvey Mudd College

• Damien Blond

• Alim Fazal

• Tory Richard

• Jim Gast

• Bryan S. Morse

• Gerald McGrath

• Vanessa S. Blake

• Many sources of slides from Internet

http://www.cheng.cam.ac.uk/seminars/imagepro/

• 533 Text book

• http://sern.ucalgary.ca/courses/CPSC/533/W99/

presentations/L2_24A_Lee_Wang/

http://sern.ucalgary.ca/courses/CPSC/533/W99/

presentations/L1_24A_Kaasten_Steller_Hoang/main.htm

http://sern.ucalgary.ca/courses/CPSC/533/W99/

presentations/L1_24_Schebywolok/index.html

http://sern.ucalgary.ca/courses/CPSC/533/W99/

presentations/L2_24B_Doering_Grenier/

• http://www.geocities.com/SoHo/Museum/3828/

optical.html

• http://members.spree.com/funNgames/katbug/

Sources

Sources•Bryan S. Morse

•Prof. Paolo Dario

•Cecilia Laschi

•Many WWW sources

•Anup Basu, Ph.D. Professor, Dept of Computing Sc.

University of Alberta

• Professor Kim, KAIST

• Computer science, University of Massachusetts,

Web Site: www-edlab.cs.umass/cs570

Companies in Canada• BIOLOGICAL

– Morphometrix (Toronto): automated pap smear testing

• DEFENSE/INTELLIGENCE

– SPAR Aerospace (Toronto, Edmonton): detection of targets in radar clutter

• DOCUMENT PROCESSING

– NCR: check processing; character recognition

• FACTORY AUTOMATION

– IPS Automation (Markham): automated bottle thread and CRT inspection

– IO Industries (London): image acquisition

• MEDICAL

– Forward Imaging (London)

– EVS (London)

– Cedara Software (Mississauga)

– Mitra (Waterloo)

– Dicomit (Markham)

• ...

Summary

Should know following terms:

• digital image (pixel, gray level)

• colormap

• digitization

– continuous-tone image

– sampling

– quantization

• dynamic range

– spatial resolution

• pixelation

– brightness resolution

• posterization & brightness contouring

• digital image processing

• digital image analysis

Outline• Human Vision and Machine

Vision

• Digital Image

• Image Formation

•Image Processing Operations

for Early Vision

•Applications of Early Image

Processing

•Extracting 3D Information

using Vision

•Using Vision for

Manipulation and Navigation

•Object Representation and

Recognition

•Perception

• Light and Optics

– Pinhole camera model

– Perspective projection

– Thin lens model

– Fundamental equation

– Distortion: spherical & chromatic

aberration, radial distortion

– Reflection and Illumination: color,

lambertian and specular surfaces,

Phong, BDRF

• Sensing Light

• Conversion to Digital Images

• Sampling Theorem

• Other Sensors: frequency,

type, ….

Start from

Scanner

Photo card

reader

Computer

Examples of simple Examples of simple Examples of simple Examples of simple image processingimage processingimage processingimage processing

What do I do with it?

Option 1

• Open it in PhotoEditor

• Print it out to Deskjet printer

• Laminate it

• Stick it on your poster

What do I do with it?

Option 2

• Open it in PhotoEditor

• Crop and resize to the size it will

appear in the final product

• Import image into Word

• Print report from Word

What do I do with it? - Option 3

• Open it in PhotoEditor

• Crop and resize to the size it will

appear in the final product

• Import image into PowerPoint

• Use PowerPoint for presentation or

• Take PowerPoint file to P&IS for

printing as a poster (this costs money)

Don't copy and paste images

into Word and PowerPoint

Always use

Insert

Picture

From file

Don’t just insert it into your document and

resize by dragging its sizing handles

If you make it

look smaller,

it’s still same

filesize

and if you make it bigger…...

Also, for images in web pages…

<img src="dept1.gif" width=274

height=230 alt="department with

daffodils">

don’t use these fields to scale image

In this class, it is required to create web pages and good documentation to your project with color pictures. This is a standard among roboticists.

Further information on web graphics

•The Web Developers Virtual Library

http://WWW.Stars.com/Authoring/Graphics/

•Graphics links http://www.stars.com/Vlib/Multimedia/Graphics.html

•Tips and tutorials for creating graphics in

PhotoShop and other applications.

http://www.mccannas.com/pshop/menu.htm

Scanning tips

•http://www.scantips.com