1. Digital Image Processing Introduction

2. Course Info Course Outline Course website
Course website
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3. Overall Motivation 1
Improvement of pictorial information for human interpretation
Improve digitized newspaper pictures
Circa 1920: 5 gray levels; 1929 : 15 levels
1960s correct for distortions introduced by on-board cameras
Modern – X-rays, pollution, pattern recognition, art work, micro-arrays, sharing of images (collaboration)
Panoramic views
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4. Overall Motivation 2
Processing of scene data for autonomous machine perception (Machine Visual Perception)
Automatic Character Recognition
Courtesy Amount Reading in checks – what error rate is acceptable?
Industrial robots, screening of x-rays and blood samples, crop assessment
Object Recognition, Planning and Communication
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5. Overall Motivation 3
Build systems that use imaging as an enabling technology
Remote desk top
Street location recognition
Parking lot management
GIS
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6. System Overview Display Hard copy Transmission Remote Processing &
Storage
Display
Hard copy
Transmission
Remote
Processing &
Object/
scene
Image
Capture
(Acquisition)
CPU
User
Interaction
Image
Sensor
A/D
Conversion
Frame
Processor
IP Workstation
Zoom
Scroll
IP functions
(MATLAB)
Storage Requirements Large
Processing: Real time 60 frames per sec/
On-line/Off-line
Enhancements
-Parallel
-Pipeline
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7. Sensors What is measured?
Visual – intensity: Luminance of object in the scene
Thermal – temperature: infra red
Xrays – absorption characteristics
Ultrasonic scanning
Laser scanners: 3-D images
Radars
Magnetometers
Gravity meters
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8. Assessing Sensor Quality
Resolution
Uniformity of grid
2-D or 3-D images
Indirect measurement
Noise effects
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9. Applications Active vs Passive Remote sensing via satellite
Agriculture monitoring
Land use
Weather
Flood and fire control
Defense intelligence
Environment monitoring
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10. Business / industry Scanning Re-use Multiple locations Security Fax
Robots
Industry, defense, consumer, environment
Medical
Patient screening and monitoring, treatment planning
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11. Overall tasks of IP & CV Object Recognition Planning Communications
Unmanned vehicles / intelligent robotics:
Perception, planning and action cycle
Architecture
Sensors
Algorithms
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12. IP Problems Image Representation and Modeling Image Enhancement
Image Restoration
Image Analysis
Image Reconstruction
Image Compression
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13. Image Representation and Modeling
Fidelity or intelligibility criteria
Design and evaluation of imaging sensor
Sampling of a BW TV signal
Models of perception
Contrast, color, spatial frequencies
Sampling rate, quantization levels and errors
Represent images as a combination of basic images
Characterization of local behaviors
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14. Image Representation and Modelling
Perception Models
Fidelity
Temporal perception
Scene perception
Local models
Image quantization
Deterministic (transforms)
Statistical (time series)
Global
AI / Scene analysis
Sequential and clustering
Image understanding
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15. Fidelity of Image Sampling
How to assess fidelity? Often based on quality measures.
Reconstruction image from sampled image.
Black and white TV signal has about 4 MHz bandwidth. What rate should it be sampled?
Sampling rate (Nyquist) > 8,000,000 per sec
Frames samples in 1/30 sec
Samples per frame = /30 = 266,000
No of lines per frame = 525
No of samples per line = 266,000/525=500
If 512 lines per frame, then 512 samples / line
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16. Image Enhancement Reduce noise Accentuate certain image features
Techniques
Contrast enhancement
Edge enhancement
Noise filtering
Sharpening
Magnifying
Methods are usually iterative and application dependent
Picture of mars, X-ray
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17. Image Restoration - 1
Objective is to minimize or remove known degradations in the image.
Sensor induced –
noise,
geometric distortions
non-linearities
Camera calibration
Given Image and Sensor Transfer Function estimate the object
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18. Image Restoration - 2 Transfer Function Object f(a,b) Sensor
h(x,y;a,b)
Image
g(x,y)
Objective: Given blurred image g(x,y), PSF{h(,;,)} and noise characteristics
Find f(a,b)
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19. Enhancement – Restoration
Similar objective
Enhancement – more heuristic
Restoration – mathematical model driven
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20. Image Reconstruction from Projections
Projections to 3-D rendering
CT scanners
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21. Image Encoding and Compression
Image potentially require large storage – maybe GBs
1K x 1K x 12 bits/pixel requires 1.5MB
Can we reduce the number of bits per pixel?
Impact on quality
Fidelity
Lossless vs lossy
Applications in telemedicine, videoconferencing, …
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22. Image Analysis Making quantitative measurements from images
Input to the object recognition, planning tasks
Often relies on segmentation
Isolates objects
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23. Image Segmentation Edge detection Region growing
Occlusion, overlapping objects
Scale and rotation
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