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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.© 1992–2008 R. C. Gonzalez & R. E. Woods Dr. Rowayda A. Sadek

IT441: Digital Image Processing

Dr. Rowayda A. [email protected]


• Textbook:– R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, 3rd Ed.,

Prentice-Hall’2007

– Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Second Ed., Prentice-Hall’2002

– Text book website: http://www.imageprocessingbook.com/

– Rafael Gonzalez, Richard Woods, Steven Eddins, “Digital Image Processing Using MATLAB”, Second Ed., Prentice-Hall’2004

• Additional/Optional Textbooks:– Anil K. Jain, "Fundamentals of Digital Image Processing", Prentice

Hall, 1989.

– W. K. Pratt, “Digital Image Processing”, Wiley-Interscience, 3rd Ed., 2001.

– K. R. Castleman, “Digital Image Processing”, Prentice Hall, 1995.

– Any other book with a similar title is fine

Course Resources


Tentative Syllabus • Introduction (Chap. I): overview of image processing

• Image Formation (Chap. II): understanding image formation and visual perception allows to design good algorithms for image processing

• Image Enhancement (Chap. III & IV): find techniques to reduce noise and emphasize and sharpen image features for display and analysis.

• Color Image Processing• Image Transforms• Image Morphological and

Segmentation


Important Learning Outcomes

At the completion of the course, students will be able to…

• Acquire the fundamental concepts of a digital image processing system

• Identify and exploit analogies between the mathematical tools used for 1D and 2D signal analysis and processing in spatial and time domain as well as frequency domain

• Design and implement with Matlab algorithms for digital image processing operations such as histogram equalization, enhancement, restoration, filtering, and de-noising

• Design and implement an applied image processing project in a useful application; medical, remote sensing, agricultural, transportation,…etc.


Grade distribution• Home work assignments

• Home work projects

• Mid term exams

• Course project– Project idea and contents , – project proposal, – final project presentation / Poster ,– Final code,– final report.

• Final Exams


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How to Achieve• PrerequisitesPrerequisites

– Linear algebra &Probability Linear algebra &Probability

– Emphasis on programming projects! Emphasis on programming projects!

– Building something from scratch (Matlab/OpenCV,….)Building something from scratch (Matlab/OpenCV,….)

• Multimode teaching interfaceMultimode teaching interface

– course material in a variety of forms: Powerpoint, MATLAB course material in a variety of forms: Powerpoint, MATLAB demo, board, email, … …demo, board, email, … …

• Teaching is two-way communication Teaching is two-way communication

– Need to know your background better (survey)Need to know your background better (survey)

– Have 10 minutes quiz to collect the feedback from time to timeHave 10 minutes quiz to collect the feedback from time to time

• Choose your own project, implement it (any platform), enjoy and Choose your own project, implement it (any platform), enjoy and gain marks and high gradegain marks and high grade

Learn and Enjoy the Course!


What is an image???

• An image is a spatial representation of an object, a 2-D or 3-D scene of another image


Computer Representation of Images

• A picture function f(x,y) is a real-valued function of two variables, having values that are nonnegative and bounded,

0 ≤ f(x,y) ≤ L-1 for all (x,y)• When a picture is digitized, a sampling process is

used to extract from the picture a discrete set of samples, and then a quantization process is applied to these samples



• Resultant digital picture function f, or digital picture, can be represented by a two-dimensional array of picture elements, or pixels

• The digital picture function f can be regarded as a mapping from {0, ... , M-1}X{0, ... , N-1} to {0, ... , L-1}

• The set {0, ... , L-1} is called the gray level set, and L is the number of distinct gray levels– For example, if we use 3 bits to represent each pixel,

then L is 8– If 8 bits are used, L is 256.



255 235 180 14

245 220 140 10

24 35 25 14

10 8 8 6

x x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x x

pixel

• A digital image is represented by a matrix of numeric values, each representing a quantized intensity value.


A Historical Overview of DIP

Newspaper industry used Bartlane cable picturetransmission system to send pictures by submarine cable between London and New York in 1920s


Early Improvement

The number of distinct gray levels coded by Bartlane system was improved from 5 to 15 by the end of 1920s


Digital Computers and DIP

• Are the images in previous slides digital??

• What do we mean by Digital Image Processing– Processing digital images by a digital computer

• DIP has been dependent on the development of digital computers and other supporting technologies (e.g., data storage, display and transmission)


Digital Image and Digital Image Processing

• Image – A two-dimensional signal that can be observed by human visual system

• Digital image – Representation of images by sampling in time and space.

• Digital image processing – perform digital signal processing operations on digital images


Soar Into Outer Space

The first picture of moon by US spacecraft Ranger 7on July 31, 1964 at 9:09AM EDT


The Born of Computer Tomography

Sir Godfrey N. Housefield and Prof. Allan M. Cormackshared 1979 Nobel Prize in Medicine for the invention of CT


CT: Computer Tomography

• Section through Visible Human Male - head, including cerebellum, cerebral cortex, brainstem, nasal passages (from Head subset)

• DIP techniques applicable:– Enhancement

– Segmentation


Ultrasound Image

• Profiles of a fetus at 4 months, the face is about 4cm long

• Ultra sound image is another imaging modality

• The fetal arm with the major arteries (radial and ulnar) clearly delineated.http://www.parenthood.com/us.html


• Acquisition – Digital cameras, scanners – MRI and Ultrasound imaging– Infrared and microwave imaging

• Transmission– Internet, wireless communication

• Display– Printers, LCD monitor, digital TV

Recent Digital Images technologies and applications


A Physical Perspective of Image Acquisition

• Extend the capabilities of human vision systems– From visible spectrum to non-visible

electromagnetic power spectrum– From close-distance sensing to remote sensing


Electromagnetic spectrum


VisiblePhotography

Motion Pictures

Biometrics

Light Microscopy

Microprocessor (60)

Remote Sensing

America at night (Nov. 27, 2000)


Thermal Imaging

Human body dispersesheat (red pixels)

Different colors indicatevarying temperatures

Operate in infrared frequency


Radar Imaging

Mountains in Southeast Tibet

Operate in microwave frequency


Magnetic Resonance Imaging (MRI)

knee spine head

Operate in radio frequency


X-ray Imaging

chest head

Operate in X-ray frequency


Other Non-Electro-Magnetic Imaging Modalities

• Acoustic imaging– Translate “sound waves” into image signals

• Electron microscopy– Shine a beam of electrons through a speciman

• Synthetic images in Computer Graphics– Computer generated (non-existent in the real

world)


Acoustic Imaging

potential locations of oil/gas

visible seismic


Electron Microscope

2500 Scanning Electron Microscopy (SEM) image of damaged integrated circuit

(white fibers are oxides resulting from thermal destruction)


Cartoon Pictures (Non-photorealistic)


Synthetic Images in Gaming

Age of Empire III by Ensemble Studios


Virtual Reality (Photorealistic)


Mixture of Graphics and Photos

Morgantown, WV in Google Map


Imaging Modalities

• Various imaging modalities help us to see invisible objects due to– Opaqueness (e.g., see through human body)– Far distance (e.g., remote sensing)– Small size (e.g., light microscopy)

• Other signals (e.g., seismic) can also be translated into images to facilitate the analysis

• Images are important to convey information and support reasoning

A picture is worth a thousand words!


Digital Image Processing Fundamentals


General Purpose Image Processing System


Image Display and Perception

• Display– CRT, LCD, DLP, Plasma, LCOS, D-ILA– HDTV, display wall– PDA, cellular phone, Gameboy– Stereoscopic (3D)

• Perception– Human Vision System (HVS)– Vision-related diseases and healthcare


CRT: Cathode Ray Tube

Mitsubishi WS-55813 Rear Projection CRT

Sony KV34XBR910 Direct View CRT


LCD: Liquid Crystal Display

Philips 42FD9954 Flat Screen LCD Display

Future: bigger, faster, cheaper


Display on Mobile Devices

cell phone

portable DVD

Gameboy

PDA


3D Display


Why DIP?

• One picture worth 1000 words!

• Support visual communication

• Facilitate inspection, diagnosis of complex systems– Human body

– Manufacturing

• Entertainment • Keep record, history• Managing multimedia

information• Security,

– monitoring,

– watermarking, etc


DIP Tour Guide

Image Compression

Image Manipulation

Image Analysis

Image Acquisition

Image Perception

Image Display

Image Generation

D.I.P.


Why D.I.P.?• Reasons for compression

– Image data need to be accessed at a different time or location– Limited storage space and transmission bandwidth

• Reasons for manipulation– Image data might experience non-ideal acquisition, transmission or

display (e.g., restoration, enhancement and interpolation) – Image data might contain sensitive content (e.g., fight against piracy,

counterfeit and forgery) – To produce images with artistic effect (e.g., pointellism)

• Reasons for analysis– Image data need to be analyzed automatically in order to reduce the

burden of human operators – To teach a computer to “see” in A.I. tasks


Compression

JPEG (CR=64) JPEG2000 (CR=64)

discrete cosine transform based wavelet transform based

•Lossless Compression/ Lossy Compression


salt and pepper (impulse) noise

Image Manipulation

Noise Removal: Noise contamination is often inevitable during the acquisition

Deblurring: License plate is barely legible due to motion blurring

Image Interpolation: Resolution enhancement can be obtained by common image processing software such as Photoshop or Paint Shop Pro

small

large

digital zooming

1M pixels

4M pixels


Image Manipulation

Image Watermarking

=+Image Mosaicing: Merge multiple images of the same scene into one

Error Concealment: blocks contaminated by channel errors

Original image Modified image


Image Analysis

Edge Detection

Face Detection

Image Matching in Biometrics: Two deceivingly similar fingerprints of two different people


Image Analysis

Image Segmentation

Object Recognition: License number can be automatically extracted from the image of license plate

Object Recognition in Military Applications


Image Analysis

Event Recognition: Image-based monitoring system prevents drowning


Image Analysis

Video Summarization: Only send out “important” motion pictures such as home-runs


Image Analysis

Content-based Image Retrieval: retrieved building images

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Data & Analytics

image features

rafael gonzalez

sadek it441

sadek textbook

prentice hall

project proposal

final exams

programming projects