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Lecture about Computer Graphics to do with IP
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The U
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COMP27112Computer Graphics and Image Processing
Lecture B1
Introduction to Image Processing
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Contact Details
• Room 2.107
• 63376
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Outline of Lectures
• Image Representation
• Point Processing
• Region Processing (2 Weeks)
• Image Processing
HistoryWhat can we doResolutionColour
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Outline of Lectures
• Image Representation
• Point Processing
• Region Processing (2 Weeks)
• Image Processing
Grey value transformsHistogramThresholdingZooming
Co-Ordinate transformsAffineNon-Affine
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Outline of Lectures
• Image Representation
• Point Processing
• Region Processing (2 Weeks)
• Image Processing
ConvolutionSmoothingEdge detectionTemplate MatchingRank Order Filters
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Outline of Lectures
• Image Representation
• Point Processing
• Region Processing (2 Weeks)
• Image Processing Region DescriptionFinding BlobsMeasurements
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Handout
• Backs up these slides
– More material
– More detailed
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Outline of Lab Schedule
• Coursework Assignments
– A mark for submission
– Unsupervised
• Laboratory classes
– 5% per submission
– Supervised
• Details in handouts on the website
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Lecture B1
• Definition
• Image Processing History and Examples
• Representation
• Colour
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What is Image Processing?
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Historical Digital Images
• 1921
• First digital images, for telegraphic transmission
• Five grey levels
• 3 hours to transmit
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Early Digital Image Processing
• 1960s
• Tied to development of hardware and software
• Space exploration
– to correct for lens distortion
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Data Sources
• Anything that can generate a spatially coherent measurement of some property can be imaged
• Can use many energy sources
• Electromagnetic
� γ rays, X rays, visible, microwave, radio
• Others
– sound, magnetic fields
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γ ray images
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X ray images
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UV
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Visible, IR
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Microwave and Radar
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LIDAR
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Applications
• In many areas
– for mapping
– exploration
– recording
– measurement
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Medicine
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Oil Exploration
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Astronomy
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Weather
Katrina
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Industry
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Security
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Satellite Images
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Satellite Images
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Face Recognition – Biometric Passports
You recognise faces
Why not automate?
Passport stores measurements of your face
Scanner captures an image and replicates the measurements
If they match – you’re in
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Image Capture
• Many sources
– So we’ll ignore them
– Ultimately they deliver a digital image
• A rectangular array of integer values
• Resolution
– Array dimensions
• Related to the scene
– The “integer values”
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Image Resolution
• How many pixels?
– Spatial resolution
• How many shades of grey/colours?
– Amplitude resolution
• How many frames per second?
– Temporal resolution
• Nyquist’s theorem
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Spatial Resolution
n, n/2, n/4, n/8, n/16 and n/32 pixels on a side.
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Field of View
NumberOf Pixels
Resolution = x⁰ per pixel
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Nyquist’s Theorem
• A periodic signal can be reconstructed if the sampling interval is half the period
• An object can be detected if two samples span its smallest dimension
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Sine wave
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Amplitude Resolution
• Humans can see:
– About 40 shades of brightness
– About 7.5 million shades of colour
• Cameras can see:
– Depends on signal to noise ratio
– 40 dB equates to about 20 shades
• Images captured:
– 256 shades
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Shades of Grey
256, 16, 4 and 2 shades.
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Colour Representation
• Newton
– White light composed of seven colours
• red, orange, yellow, green, blue, indigo, violet
• Young etc.
– Three primaries could approximate many colours
• red, green, blue
– CIE
• Define three primary colours: x, y, z (reddish, greenish, blueish)
• These match HVS
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CIE Primaries
0
0.5
1
1.5
2
2.5
350 450 550 650 750
sen
sit
ivit
y
wavelength
CIE colour matching
Series1
Series2
Series3
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CIE Colour Diagram
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Other Colour Models
• YCrCb
– Intensity and Colour difference
• Used in broadcasting
• Perceptual Spaces
– HSV | IHS | HSB
– Lab
• All separate intensity/brightness and chromaticity
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YCrCb
• Part of the standard for digital video
• Less detail in Cb, Cr so fewer pixels stored
• One Cb and one Cr pixel per four Y pixels
1280813.04187.05.0
1285.03313.01687.0
114.0587.0299.0
+−−=
++−−=
++=
BGRCr
BGRCb
BGRY
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Why Perceptual Spaces?
• Equal distances on CIE diagram DO NOT correspond to equal changes in perceived colour
• This is important for measurement
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IHS
• Intensity
– Aka Brightness
– Weighted average of RGB
• Hue
– The basic colour
– An angle from 0 to 359
• Saturation
– Depth of colour
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Lab
• Also known as L*a*b* and CIELAB
– Used in Photoshop
• Can represent all perceivable colours
– Because it’s derived from CIE XYZ co-ordinates
• Device independent
• L* represents lightness
– 0 = black, 100 = white
• a* represents green � magenta
• b* represents blue � yellow
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Summary
• Chapters 1 and 2 of handout
• Sample applications
• Resolution
• Colour models
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640k ought to be enough for anybody
Bill Gates, 1981
