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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: [email protected]
– 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