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TDI2131 Digital Image Processing

Introduction to Image

Processing

Lecture 1

John See

Faculty of Information Technology

Multimedia University

Some portions of content adapted from Zhu Liu, AT&T Labs

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Lecture Outline

● Course Information

● Introduction & Overview

● Applications of Image Processing

● Fundamental Image Processing Operations

● More Applications...

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Course Instructor

● John See

– Office: BR 3005

– Email: johnsee@mmu.edu.my

– Tel (o): 03-83125478

– http://pesona.mmu.edu.my/~johnsee

– Consultation Hours: Wednesdays, 2-6pm

● All other times by appointment

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Textbook

● Digital Image Processing

(3rd Edition) – Gonzalez &

Woods

● The version in the

bookstore has a purple

cover instead of red

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Other References

● Introduction to Digital Image

Processing with MATLAB, A.

McAndrew (2004)

● Fundamentals of Digital

Image Processing, A.K. Jain

(1990)

● Course notes from other

universities available online

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Lecture & Tutorial

● Lectures: Every Thursday 4-6pm, CR2003

● Tutorials: AR 2003 (GVGD Lab)

– Every Friday, 10am-12pm

– Theory & MATLAB exercises

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Grading

● Assignments 30%

– Assignment 1 (8%)

– Assignment 2 (10%)

– Assignment 3 (12%)

● Term Test 10%

● Final Exam 60%

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Coursework

● Term Test: in Week 12 (most likely)

● Assignments (using Matlab)

Assignment 1 (8%)

X-ray Enhancement

due Week 7

Assignment 2 (10%)

Fingerprint Feature Extraction

due Week 11

Assignment 3 (12%)

Automated Color-based

Face Detection

due Week 14

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Housekeeping

● Attendance will be taken in both lecture and tutorial. Signing

for someone other than yourself is prohibited.

● Plagiarism (especially in assignments) is an offence. You can be

failed or suspended academically if found to have plagiarised

others.

● Participation in class is highly encouraged. Some bonus marks

may be awarded to you discretely.

● Syllabus is non-exhaustive. There is always much more

topics/areas that may not be covered within the limits of this

course, so self-exploration is highly encouraged!

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Overview

● Early days of computing – data was numerical

● Later, textual data became more common

● Today, many other forms of data: voice, speech,

images, video, web, wireless packets, etc.

● Each of these types of data are signals.

● Loosely defined, a signal is a function that conveys

information.

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Relationship of Signal Processing to

Other Fields● People have tried to send or receive signals through

electronic media – telegraphs, telephones,

television, radar, etc. --- signals affected by the

system used to acquire, transmit, or process them.

● Systems can be imperfect and introduce noise,

distortion, or other artifacts

● Finding a way to correct them is fundamental in

signal processing

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Relationship of Signal Processing to

Other Fields● To send specific signals/messages to others,

information content is introduced into the signal

and hopefully, we can extract them later!

● Where do we find these signals?

– Signals encoded from natural phonemena (audio signals,

images from photographs, scenes from video footage)

– Signals created synthetically, man-made (speech

generation, computer graphics)

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Concerned Fields in Signal Processing

● Digital Communication (Wired & Wireless)

● Data Compression

● Speech Synthesis & Recognition

● Computer Graphics

● Image Processing (sometimes with video processing)

● Computer Vision

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Fields that deal with Images

● Computer Graphics: Creation of images synthetically

● Image Processing: Enhancment or manipulation of

the image – the result of which is usually another

image

● Computer Vision: Analysis and understanding of

image content

● Video Processing (new!): Similar with image

processing, but processing of multiple

images/frames. Combined with computer vision, end

result is normally extracted information

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3 Principal Uses of Image Processing

● Improvement of pictorial information for human

interpretation

● Compression of image data for storage and

transmission

● Processing of image data for autonomous machine

perception to enable object representation,

detection, classification and tracking

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Categorisation by Image Sources

● Radiation from Electromagnetic Spectrum

● Acoustic

● Ultrasonic

● Electronic (in the form of electron beams used in

electron microscopy)

● Computer (synthetic images used for modeling and

visualization)

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What are some applications

that make use of Image

Processing?

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Typical Areas of Application

● Television Signal Processing

● Satellite Image Processing / Remote Sensing

● Medical Image Processing

● Robotics

● Visual Communications

● Law Enforcement

● Automatic Visual Inspection for Manufactured Goods

● Etc.

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Television Signal Processing

● Image brightness, contrast, color hue adjustment

● Video compression for efficient delivery and storage

● Conversion among different video formats

– QVGA <-> VGA <-> XVGA

– SDTV <-> HDTV

– NTSC <-> PAL

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Medical Image Processing

● Images are acquired to get information about Anatomy and Physiology

of a patient

● How to reconstruct the image from captured data

● How to process/analyze the image to help diagnosis/treatment?

– Ultra Sound (US)

– Magnetic Resonance Imaging (MRI)

– Positron Emission Tomography (PET)

– Computer Tomography (CT)

– X-Rays

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Visual Communication

● Videophone

● Tele-conferencing

● Tele-shopping

● How to compress the video to

reduce bandwidth/storage

requirements?

● How to conceal artifacts due to

transmission losses?

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Law Enforcement

● Biometric Identification /

Verification

– Fingerprint

– Face

– Iris

● How to extract features that

can be used to differentiate

among different images?

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Law Enforcement

● Paper currency or cheque

fraud

– Automated counting or

reading of serial number

for tracking and

identifying bills

● Automated license plate

reading

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Robot Control

● Automatic Maneuvering

● Unmanned Operations

– Autonomous Vehicle

Driving

● How to detect and track

target?

● How to avoid obstacles?

Mars Rover

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Automated Visual Inspection

● Manufactured Goods

– Circuit board – missing parts

– Pill container – missing pills

– Bottles – filled up levels

– Bubbles in clear-plastic product –

detect unacceptable air pockets

– Cereal – inspection for color, presence

of burnt flake

– Image of replacement lens for human

eye – inspection of damaged implants

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Satellite Image Processing

● Remote sensing

● Climate

● Geology

● Land resource

● Flood monitor

● How to enhance the image to

facilitate interpretation?

● How to analyze the image to

detect certain phenomena?

New York (from Landast-5 TM)

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Remote Sensing

● Weather Observation and

Prediction

Multispectral image of Hurricane Andrew

from satellites using sensors in the

visible and infrared bands

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Acoustic Imaging

● Cross-sectional image of a seismic model. The arrow

points to a hydrocarbon (oil and/or gas) trap (bright spots)

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Components in Digital Image Processing

We will deal mainly with most of the light green boxes.

Yellow boxes belong to “computer vision” and “pattern

recognition”

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Image Acquisition

● Camera

– Consist of 2 parts

● Lens: Collects appropriate

type of radiation emitted from

object of interest and forms

and image of the real object

● Semiconductor device:

Charged-coupled device (CCD)

which converts the irradiance

at image plane into an

electrical signal

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Image Acquisition

● Framegrabber

– Needs circuits to digitize the

electrical signal from the

imaging sensor to store the

image in the memory (RAM) of

the computer.

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3 Levels of Image Processing

● Low-level Processing: input & output are images

– Primitive operations such as image preprocessing to reduce noise,

contrast enhancement and image sharpening and smoothing.

● Mid-level Processing: input may be images, output are attributes

extracted from those images

– Segmentation, description of objects, classification of individual

objects

● High-level Processing

– Image analysis and understanding of content, representation and

recognition of extracted patterns from images

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Basic Image Processing Operations

● Simple Point Processing

● Image Enhancement

● Image Restoration

● Noise Reduction

● Colour Image Processing

● Image Segmentation

● Morphological Image Processing

● Etc.

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Simple Point Processing

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Image Enhancement

● To bring out details that are obscured, or to highlight certain features

of interest in an image

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Image Enhancement

● Negative transformation of a digital mamogram. Note that the

cancerous region (dark spot) in the right image is enhanced.

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Image Restoration

● Improving appearance of an image. Tend to be based on mathematical

or probabilistic models of image degradation

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Image Restoration: Noise Reduction

● Improving appearance of an image. Tend to be based on mathematical

or probabilistic models of image degradation

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Colour Image Processing

● Colour is a powerful descriptor that often simplifies object

identification and extraction from a scene.

● Human can discern thousands of colour shades and intensities,

compared to about only two dozen shades of gray.

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Image Segmentation

● Attempts to separate certain objects of interest from the image

background or other objects – One of the most difficult tasks in DIP!

● Output of the segmentation stage is raw pixel data, constituting either

the boundary of a region or all the points in the region.

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Image Segmentation

● Edge Detection

● Colour Region Segmentation

Extraction of settlement

area from aerial imagery

Ground replacement due to

earthquake in California, 1992

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Wavelets and Multi-resolution

Processing● Foundation of representing images in various degrees of resolution.

● Used in image data compression and pyramidal representation (images

are subdivided successively into smaller regions).

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Image Compression

● Reducing storage required to save an image or the bandwidth required

to transmit it.

● JPEG, JPEG2000, JBIG2

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Morphological Image Processing

● Mathematical morphological operations

● Tools for extracting image components that are useful in the

representation and description of shapes.

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Image Morphing (Metamorphosis)

● Transformation of one digital image to another. Special visual effect in

the entertainment industry!

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Image Stitching (Mosaics)

● Blend together overlapping images to produce a panoramic stitched

image

+ +........+

=

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And now...what do you see?

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We are smarter than computers!

● How do you recognize the

banana?

● How can you get a computer

to do that?

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Representation and Description

● Representation – make a decision on how the extracted data should be

represented

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Recognition and Interpretation

● Recognition – the process that assigns a label to an object based on the

information provided by its descriptors.

● Interpretation – assigning meaning to an ensemble of recognized

objects.

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Knowledge Base

● A problem domain – detailing regions of an image where the

information of interest is known to be located

● Help to limit search, pinpoint information details to be used for further

processing.

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High-level Processing

● The two last stages in the chart (Representation & Recognition) are

often categorized as high-level processing and usually belong to the

area of Computer Vision / Pattern Recognition.

● Depending on the usage or application, the earlier stages can be used

to prepare images for high-level processing.

● Let's see what applications that considered high-level processing...

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Fingerprint Recognition

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Content-based Image Retrieval

Query

image

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Face Detection

Final Year Project 2006 – FACEFIND, Nusirwan & Chong

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Face Detection

Is it harder to detect faces in a large group of people?

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Tracking and Counting People

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Readings

● Digital Image Processing (3rd Edition), Gonzalez & Woods,

– Chapter 1

● MATLAB Getting Started Guide from Mathworks

– http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/getstart.pdf

● More tutorials to get you started (downloadable from my website

links):

– MATLAB Primer – Concise but extensive introduction to Matlab

– Two basic introductory tutorials by Gerald Recktenwald – very easy

to understand