Digital Image Processing Introduction

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Digital Image ProcessingIntroduction
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Course Info

• Course Outline
• http//cs.gmu.edu/asood/cs686/cs686-sood-a.html
• Course website
• http//cs.gmu.edu/asood/cs686

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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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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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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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System Overview
Display Hard copy Transmission Remote
Processing Display
Storage
Image Capture (Acquisition)
Object/ scene
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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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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Assessing Sensor Quality

• Resolution
• Uniformity of grid
• 2-D or 3-D images
• Indirect measurement
• Noise effects

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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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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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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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IP Problems

• Image Representation and Modeling
• Image Enhancement
• Image Restoration
• Image Analysis
• Image Reconstruction
• Image Compression

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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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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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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) gt 8,000,000 per sec
• Frames samples in 1/30 sec
• Samples per frame 8000000/30 266,000
• No of lines per frame 525
• No of samples per line 266,000/525500
• If 512 lines per frame, then 512 samples / line

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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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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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Image Restoration - 2
Transfer Function
Object f(a,b)
Sensor h(x,ya,b)
Image g(x,y)
Objective Given blurred image g(x,y),
PSFh(,,) and noise characteristics
Find f(a,b)
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Enhancement Restoration

• Similar objective
• Enhancement more heuristic
• Restoration mathematical model driven

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Image Reconstruction from Projections

• Projections to 3-D rendering
• CT scanners

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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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Image Analysis

• Making quantitative measurements from images
• Input to the object recognition, planning tasks
• Often relies on segmentation
• Isolates objects

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Image Segmentation

• Edge detection
• Region growing
• Occlusion, overlapping objects
• Scale and rotation