introduction what is “image processing and computer vision”? image representation
TRANSCRIPT
Introduction
What is “image processing and computer vision”?Image Representation
Image Processing and Computer Vision: 1 2
What is “Image Processing and Computer Vision”?
Image Processingmanipulate image datagenerate another image
Computer Visionprocess image data
generate symbolic data
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Computer Vision Reconstruction
Recover 3D information from data Recognition
Detect and identify objects Understanding
What is happening in the scene?
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Historical overview 1920s
Coding images for transmission by telegraph (3 hours)
1960s Computers powerful enough to store
images and process in realistic times Space program
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1960s - 1970s Applications
Medical imaging Remote sensing Astronomy
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Today DTV Image interpretation Biometry GIS Human genome project
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Example images (1)
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Example images (2)
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Sample applications Character recognition (OCR)
Printed text, Hand-printed text, Cursive text
Biometry GIS
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Printed Text Characteristics
Regular shape Regular orientation Good contrast
Can compare characters with a prototype
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Position
No
n-N
orm
alis
ed
Co
rre
lati
on
Template
Input Output
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Hand Printed Text Characteristics
Less regularity Must examine components of
character
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Cursive Text Totally irregular Careful analysis of strokes
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Biometry Using personal characteristics to
identify a person Fingerprints Face Iris DNA Gait etc
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Iris Scan Striations on iris are individually
unique Obvious applications:
Security PIN
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} fixed number of samples
Locate the eye in the head image
Radial resampling of iris
Numerical descriptionAnalysis
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GIS Earth/Planetary Observation
Monitoring Exploration
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Examples
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System Overview
Enhancement
Feature Extraction
Feature Recognition
Image Recognition
Captured data
Labels
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Image Representation Image capture Image quality measurements Image resolution Colour representation Camera calibration Parallels with human visual system
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Image Capture Many sources
Consider requirements of system Resolution
Type of data Transducer
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Representation Sampled data
Spatial Amplitude
On a rectangular array
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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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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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Spatial Resolution
n, n/2, n/4, n/8, n/16 and n/32 pixels on a side.
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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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Temporal Resolution Nyquist’s theorem for temporal
data How much does an object move
between frames? Can motion be understood
unambiguously?
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Colour Representation Newton
White light composed of seven colours
red, orange, yellow, green, blue, indigo, violet
Three primaries could approximate many colours
red, green, blue
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CIE Colour Diagram
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Other Colour Models YMCK IHS YCrCb etc
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Camera Calibration Link image co-ordinates and world
co-ordinates Extrinsic parameters
Location and orientation of camera with respect to a co-ordinate frame
Intrinsic parameters Relate pixel co-ordinates with camera
reference frame co-ordinates
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Extrinsic Parameters Camera’s
Location Orientation
With respect to world origin
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World frame
Camera frame
translate androtate
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Intrinsic Parameters Characterise
Optical Geometric Digital
properties of camera Relate
Image co-ordinates to camera co-ordinates
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Pinhole Camera
Image
ObjectOpticalcentre
Image and centre, object and centre are similar triangles.
f Z
Z
Yfy
Z
Xfx
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Distortionless If
no distortions uniform sampling
Co-ordinates linearly related offset and scale
s yo yyimy
sxoxximx
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Distorted Periphery is distorted
k2 = 0 is good enough
yxr
rkrkydy
rkrkxdx
222
42
211
42
211
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Parallels With Human Visual System Image capture
Retina Focussing
Cornea and lens Exposure
Iris and retina
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Summary Historical overview Sample applications Resolution Colour models Camera calibration
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640k ought to be enough for anybodyBill Gates, 1981