Day 1
9.00 am – 12.00 pm
Describe the key concepts of machine vision systems
- Explain the goals of machine vision systems in factory automation
- Describe the main components of machine vision systems according to the working environment and requirements
- Describe the various lighting systems used in machine vision systems
- Distinguish between image processing and image analysis
- Define the terminologies of image formation and focusing
12.00 am – 1.00 pm
Lunch Break
1.00 pm – 5.00 pm
Comprehend the fundamentals of image processing techniques used in machine vision systems
- Explain the properties of images for processing
- Distinguish between frequency and spatial domains
- Describe the concept of Fourier Transformation in imagine processing
- Define the histogram and threshold methods used in image processing
- Describe the convolution mask method in image enhancement, noise reduction, filtering and connectivity
- Describe segmentation by region growing and region splitting
- Define morphology operations in image processing
Day 2
9.00 am – 12.00 pm
Apply image analysis techniques in machine vision systems
- Describe elementary object recognition and identification by features
- Compare scene analysis and mapping
- Apply the detection and localization of edges
- Describe stereo imaging techniques in machine vision system
- Describe specialized lighting techniques in depth measurement
12.00 am – 1.00 pm
Lunch Break
1.00 pm – 5.00 pm
Apply Pattern Recognition in the domain of Machine Vision and in a manufacturing setting
- Explain Pattern Recognition as a class of Machine Learning
- Apply a software platform for Pattern Recognition
- Perform image formation and preprocessing
- Apply feature extraction from images
- Apply labelled data and unlabelled data in the design of Pattern Recognition systems for Machine Vision via supervised learning and unsupervised learning
- Apply Pattern Recognition concepts to develop simple Machine Vision Automation Systems
Day 3
9.00 pm – 12.00 pm
Analyse machine vision examples using Regression, Clustering and Classification
- Analyse a Machine Vision problem using the Regression, Clustering and Classification techniques
- Apply Pattern Recognition techniques to a Machine Vision model
12.00 am – 1.00 pm
Lunch Break
1.00 pm – 5.00 pm
Analyse machine vision examples using Regression, Clustering and Classification (Continue)
- Compare the performance of the pattern recognition techniques for the given Machine Vision problem
- Analyse the results and suggest possible improvements
Day 4
9.00 pm – 12.00 pm
Develop a machine vision and pattern recognition application
- Identify the appropriate applications for vision guided factory automation
- Perform the setting up of a machine vision system to acquire an image
12.00 am – 1.00 pm
Lunch Break
1.00 pm – 5.00 pm
Develop a machine vision and pattern recognition application (Continue)
- Set the boundary conditions of machine vision system for industrial automation
- Develop a machine vision and pattern recognition manufacturing application
Written Assessment (Open Book)
Certification
Participants will be issued with a Certificate of Accomplishment upon meeting 75% of the required course attendance.
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