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Introduction to Image Processing
Grass
Sky
TreeTree
? ?
Introduction
A picture is worth more than a thousand words
Aims and Objectives
• A general introduction to the common techniques of image processing and its relations with Computer Vision and Computer Graphics
• To review and understand the principal approaches used, which provide as the basis for further study of the related fields
• To offer practical experience in writing programs that manipulate images, using examples in Java
Module Information
• Prerequisites: G51MCS, G51PRG or equivalent
• Course Structure– Lecture: 2 hours/week– Practical: 1 hour/week (starting
week 3/4)
• Assessment– Programming Assignment (40%)– 1-hour exam (60%)
• Handoutshttp://moodle.nottingham.ac.uk OR
http://jupiter.nottingham.edu.my/~kczshh/G52IIP/
• Reference Books
– Digital Image Processing, 3rd Edition by Rafael Gonzalez & Richard Woods
– Digital Image Processing
Using Java by Nick Efford
Image Processing, Vision, Graphics
• In image processing we do things like – removing noise from images– finding edges and features in
images– generally changing 2D images
to other 2D images
• In computer vision we do things like– finding moving objects in a
scene– recognising objects from a
database– build abstract models of the
world from images– generally extract information
about 3D world from 2D images
• In computer graphics we do things like– creating a 3D model,
with realistic shape, colour, texture, and project it to 2D for viewing
– animate the 3D model– creating a virtual world
and animate the objects in it
• The link between imaging and graphics– image based modelling
Vision and Graphics Combined
Model Real Scene
Real Cameras
Image
Output
SyntheticCamera
(Courtesy of Michael Cohen)
Another Useful Paradigm
• Three Processing Levels: Low-level process:
– primitive operations like noise reduction, contrast enhancement, image sharpening…
– input: image output: image Mid-level process:
– tasks like segmentation, representation, description– input: image output: attributes extracted from images
High-level process– “making sense” of an ensemble of recognized objects– input: image (sequence) output: interpretation
DigitalImage Processing
Computer Vision
Proposed Contents
• Fundamentals • Basic Image Manipulation• Noise and Spatial Techniques• Point, Line & Edge Detection • Image Segmentation &
Representation• Basic Spectral Techniques• Others if time permits
Words of Caution• Lecture Contents
slightly mathematical + practical attendance required to understand lecture materials not difficult but leaving it until the last minute can be
disastrous further readings are required if you intend to
undertake DIP related projects
• Reference Texts read them if you need more detailed explanations
than what is found in the lecture slides
• Tips for Passing this Module coursework must be submitted practise by solving the past exam questions show detailed steps of how the final answers are
derived in your answer scripts
Radio Images
knee spine head
visible infrared radio
• Radio Band− medicine and astronomy
Thermal Images
human body dispersesheat (red pixels)
different colours indicatevarying temperatures
• Infrared Band
Remote Sensing
hurricane Andrewtaken by NOAA GEOS
America at night (Nov. 27, 2000)
• Visible and Infrared Bands− weather and environmental observations
Light Microscopy
taxol (250) cholesterol (40) microprocessor (60)
• Visible Band− pharmaceuticals and microinspection
Other Non-Electromagnetic Imaging Modalities
• Acoustic Imaging translate “sound waves” into image signals
• Electron Microscopy shine a beam of electrons through a
specimen
• Synthetic Images in Computer Graphics computer generated (non-existent in the
real world)
Application Areas• Space
cosmic radiation, images from Hubble space telescope and interplanetary probe
• Medicine multitude of diagnostic medical images
• Remote Sensing and GIS terrain classification and meteorology
• Industrial Inspection replace human operators with machines
• Security and Law Enforcement surveillance and biometrics
• Human Computer Interaction more natural interface using face, gesture recognition
Acknowlegements
Slides are modified based on the original slide set from Dr Li Bai, The University of Nottingham, Jubilee Campus plus adoptions from the following sources:
• Digital Image Processing book by Gonzalez and Woods• robots.stanford.edu/cs223b07/notes/CS223B-L1-Intro.ppt • www.nmc.teiher.gr/activities/MASTERS/intensive/
IP_Chania/prez/TEI/bebis/CV_Overview.ppt• http://www.csee.wvu.edu/~xinl/courses/ee465/intro.ppt