1. TK 3813 TEKNIK PEMPROSESAN MEDIA DIGITAL
Lecturer : Dr. Masri Ayob
Room : E3-31
Ext : 6726

2. Course Information Text books:
Nick Efford, “Digital Image Processing: A Practical Introduction Using Java”. Addison Wesely.2000.
Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing, 2nd edition". Prentice Hall, 2001.
Paul F. Whelan and Derek Molloy, “Machine Vision Algorithms in Java”. Springer Verlag.2001.
Class:
Monday 11:00am-2:00pm BK5
Friday 8:00am-11:00am BK5
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3. Assessment Assignment 1: 10% Assignment 2: 20% Final examination 70%
Warnings:
Copying assignment is prohibited.
Delay of submission influences on marks.
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4. Lecture 1: Introduction
What are images ???
What is digital image processing ???
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5. Overview Early days of computing, data was numerical.
Later, textual data became more common.
Today, many other forms of data: voice, music, speech, images, computer graphics, etc.
Each of these types of data are signals.
Loosely defined, a signal is a function that conveys information.
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6. Overview
As long as people have tried to send or receive through electronic media: telegraphs, telephones, television, radar, etc. there has been the realization that these signals may be affected by the system used to acquire, transmit, or process them.
Sometimes, these systems are imperfect and introduce noise, distortion, or other artifacts.
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7. Overview
Understanding the effects of these systems have and finding ways to correct them is the fundamental of signal processing.
Sometimes, these signals are specific messages that we create and send to someone else (e.g., telegraph, telephone, television, digital networking, etc.).
That is, we specifically introduce the information content into the signal and hope to extract it out later.
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8. Overview
Sometimes, these man-made signals are encoding of natural phenomena (audio signal, acquired image, etc.),
but sometimes we can create them from scratch (speech generation, computer generated music, computer graphics).
Finally, we can sometimes merge these technologies together by acquiring a natural signal processing and then transmitting it in some fashion.
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9. Overview Some related fields in signal processing are:
Digital Communication
Compression
Speech Synthesis and Recognition
Computer Graphics
Image Processing
Computer Vision
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10. Images
Images are pictures: a way of recording and presenting information visually.
Pictures are important to us because they can be extraordinarily effective medium for the storage and communication of information.
‘A picture is worth a thousand words’.
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11. Images
Information about the objects in the scene is recorded as variations in the intensity and colour of the detected light.
Although a scene is (typically) three-dimensional, the image of that scene is always two-dimensional.
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12. Image
There are other forms of energy, besides light, that can be used to create image.
Light is merely the visible portion of the electromagnetic (EM) spectrum.
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13. The spectrum of electromagnetic radiation
Wavelength (/m)
10-12
10-10
10-8
10-6
10-4
10-2 1
102
104
Gamma rays
X-rays UV IR
microwaves
Radio
Visible portion
light
violet blue
green
yellow red
Wavelength (/nm)
The spectrum of electromagnetic radiation
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Note: 1 nm= 10-9 m

14. Image
Within visible region (400nm-700nm), wavelength is perceived as colour: 550nm-green, 700nm-red.
At shorter wavelengths, EM radiation caries larger energies.
For example, X-ray images can reveal the internal structure of objects that are opaque to light.
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15. Gamma-Ray Imaging
Gamma-rays are highly penetrating and, like x-rays, have medical application.
Typically, the patient ingests a substance that is ‘tagged’ with a radioactive tracer. A gamma camera collects gamma ray photons emitted by body tissues and form an image from them. Diseased tissue such as tumour will often appear as a bright region in images.
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16. Gamma-Ray Imaging Nuclear Image (a) Bone scan
(b) PET (Positron emission tomography) image
Astronomical Observations.
(c) Cygnus Loop
Nuclear Reaction
(d) Gamma radiation from a reactor valve a b c d
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17. X-ray Imaging Medical diagnostics (a) chest X-ray (b) aortic angiogram
(c) head CT (computed tomography )
Industrial imaging
(d) Circuit board
Astronomy
(e) Cygnus Loop a d b c e
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18. Ultraviolet Imaging Lithography Industrial inspection
Microscopy (fluorescence)
(a) Normal corn
(b) Smut corn
Lasers
Biological imaging
Astronomical observations
(c) Cygnus Loop a b c
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19. Imaging in Visible and Infrared Bands
Astronomy
Light microscopy
pharmaceuticals
(a). taxol (anticancer agent)
(b). cholesterol
Microinspection to materials characterization.
(c). Microprocessor
(d). Nickel oxide thin film
(e). Surface of audio CD
(f). Organic superconductor a b c d e f
Note: IR can be used to locate people or moving vehicles even in condition of total darkness.
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20. Imaging in Visible and Infrared Bands
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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21. Imaging in Visible Bandsc d e f
Industry : visual spectrum (automated visual inspection of manufactured goods):
(a). A circuit board: inspect them for missing parts
(b). Pill container: look for missing pills
(c). Bottles : look for bottles that are not filled up to an acceptable level
(d). Bubbles in clear-plastic product: detect unacceptable number of air pockets
(e). Cereal : inspection for color and the presence of anomalies such as burned flake.
(f). Image of replacement lens for human eye : inspection of damaged or incorrectly manufactured implants
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22. Imaging in Visible Bands
Law enforcement :
(a). Thumb print: automated search of a database for a potential matches.
(b). Paper currency : automated counting/reading of the serial number for tracking and identifying bills.
(c) and (d) Automated: license plate reading a b c d
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23. Imaging in Microwave Bands
Imaging radar : the only way to explore inaccessible regions of the earth’s surface.
Radar image of mountains in southeast Tibet.
Note the clarity and detail of the image, unencumbered by clouds or other atmospheric conditions that normally interfere with images in the visual band.
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24. Imaging in Radio Bands Medicine
Magnetic resonance image (MRI) : 2D picture of a section of the patient (any plane)
(a) knee
(b) spine
Astronomy.
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25. Imaging in Radio Bands
Geological applications : use sound in the low end of the sound spectrum (hundred of Hz).
Mineral and oil exploration: Cross-sectional image of a seismic model.
The arrow points to a hydrocarbon (oil and/or gas) trap (bright spots)
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26. Ultrasound Imaging Manufacturing Medicine (a) Baby
(b) Another view of baby
(c) Thyroids
(d) Muscle layers showing lesion a b c d
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27. Key Aspect of Computer & Machine Vision
Image processing, Image representation, Image analysis, Image understanding
Computer vision
Machine vision
System engineering, Robotic applied vision, etc.
Pattern recognition, Visual modelling & perception, Biological vision, etc.
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28. What is digital image processing?
Vision allows humans to perceive and understand the world surrounding us.
Computer vision aims to duplicate the effect of human vision by electronically perceiving and understanding an image.
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29. What is digital image processing?
Image processing is a subclass of signal processing concerned specifically with pictures.
Improve image quality for human perception and/or computer interpretation.
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30. What is digital image processing?
Digital image processing concerns the transformation of an image to a digital format and its processing by digital computers.
Both input/output are digital images.
Processing images to give new images.
E.g. Removing noise from images, finding edges and features in images and filling in missing information in an image.
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31. Example: Image processing
The most common kind of digital image processing is digital image editing.
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32. What is digital image analysis?
Digital image analysis is related to the description and recognition of the digital image content.
Input: digital image & output: image description.
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33. Example: Analysis
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34. Computer Vision Computer vision has three levels:
Low level vision: Primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening.
Intermediate level vision: inputs may be images, outputs are attributes extracted from those images (image representation):
Segmentation
Description of objects
Classification of individual objects.
High level vision: Algorithms have symbolic representations for both input/output. They are closely related to artificial intelligence and pattern recognition. They try to simulate the high level of human visual perception (image understanding).
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35. Computer Vision Computer vision aims to: Take an image or images.
Perform some sort of computation on them.
Provide some new information as a result.
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36. Computer Vision Human vision can give us inspiration, but so can:
Signal processing theory.
General AI (e.g. pattern recognition).
Mathematics & statistics.
Computer visual processing is not like human vision:
A digital image is not like a retinal image.
A computer is not like the brain.
We don’t even know how the brain works.
Human vision system is qualitative in nature, whereas machine/computer vision is quantitative.
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37. Applications Image reconstruction:
Images sometimes have missing information.
We can use the surrounding areas to predict what lies in the gap.
Tracking:
We often want to track objects as they move through a video sequence.
Human computer interface (HCI):
Using vision, we can monitor people and use this to drive new interfaces.
E.g. Face/eye/thumb print recognition, automated car plate reading.
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38. Questions ???
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