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Industrial Machine Vision
Course
1. Course Objectives
This
course aims to give participants a good foundation in image processing
theory as
well as the ability to apply this knowledge to the design and
implementation of
systems for machine vision in the real world. Components of machine
vision
systems that are currently available in the market will be discussed.
There will
also be hands-on sessions to perform simple machine vision tasks on an
integrated system. The course will also touch on the trends evident in
the
machine vision industry with the advent of technological advances in
processing
power and higher transistor density.
2.
Course Outline
Day-1:
Overview
of Machine Vision:
- What is and what is not
- typical tasks: presence verification,
precision gauging, print
quality inspection, bar code & data matrix identification,
optical
character recognition (OCR)
Theory of image processing:
- Terminology- Pixel, Image, Filter, Frame Rate,
Histogram
- Gray scale transformations- Look Up Tables,
Histogram Equalization
- Image Arithmetic- Addition, Subtraction, Min
& Max
- Spatial Linear Filters - Averaging, Smoothing,
Edge Detection
- Spatial Statistical Filters- Median Filters
- Morphological Filters
- Identification- Classification, Template
Matching
Components of Machine Vision System:
- Optical Lenses - Telecenteric, Microscopic,
Telephoto
- Analog and Digital Cameras - Progressive scan,
Area Scan, Line Scan, FireWire, USB, CameraLink,
GE, Special Function
- Digital and analog Frame Grabber
- Machine
Vision Software- Image processing Libraries, MV Libraries, MV
application, Hardware SDK, Embedded software, Smart camera
- MV Illumination - LED,
Fluorescent, Fiber Optics, Coaxial, Back light,
Diffused light.
- Optical Filters - Polarizer,
Analyzer, special filters
- Interfacing with other machines- Digital IO,
RS232/485, Profibus, Ethernet
Day-2:
Choosing
Sensor and Lens:
- Video Formats
- Object distance, working distance,
choosing the correct lens
- Type of lenses mount - C, CS, F bayonet
System Resolution & Achievable Accuracy:
- Sensor size
- Optical magnification
- Pixel Resolution
- System Accuracy
- Example 1 : configuring an Area Scan Camera
- Example 2 : Configuring a Line Scan System
Playing with Illumination:
- Back light
- Front light
- Diffused light
- Coaxial Illumination
- Axial polarizer
Common software
Algorithms:
- Image filtering
- Image arithmetic
- Positioning
- Classification
Day-3:
Hands
on Practicum:
- Practical 1: Presence Verification
- Practical 2: Position Alignment
- Practical 3: Gauging Measurement
- Practical 4: Pattern Recognition
using machine learning
- Practical 5: Pattern Recognition
using template matching
Industry outlook:
- Camera Link standard for digital cameras
- High resolution sensors with higher frame rates
- Smart Camera
- Image archiving - high speed logging
- New algorithms - alignments
- 3D alignment
Components of Machine Vision System:
- Optical Lenses - Telecenteric, Microscope,
Telephoto
- Analog and Digital Cameras - Progressive scan,
Area Scan, Line Scan, FireWire, USB, CameraLink,
GE, Special Function
- Digital and analog Frame Grabber
- Machine
Vision Software- Image processing Libraries, MV Libraries, MV
application, Hardware SDK, Embedded software, Smart camera
- MV Illumination - LED,
Flourescent, Fiber Optics, Coaxial, Back light,
Diffused light.
- Optical Filters - Polarizer,
Analyzer, special filters
- Interfacing with other machines- Digital IO,
RS232/485, Profibus, Ethernet
2.
Course Details
The
maximum enrollment per class is 4 delegates. Course materials and a book
entitled " Industrial Image processing - Visual Quality
Control in Manufacturing" by C. Demant et al. will be given to each
delegate for post -course reference.
Venue: Neurotech Pte Ltd ( Singapore)
Date: please call (65)-6272-2766
Time : 10.00 AM to 5:30 PM for a total 3 days
Fee: S$3,000.00 ( Singapore dollars) per participant
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Machine Vision Illumination
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Machine Vision Camera
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