52
Color Image Processing
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
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4.6 Color Image Processing
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4.6 Color Image Processing
4.6 Color Image Processing
• RGB to HSI Conversion
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4.6 Color Image Processing
• HSI to RGB Conversion
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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
• [email protected]ührung 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
