Machine Vision Training Workshops
Machine Vision (Lighting, Optics and Cameras)
Session Details
» HRDF/HRDC claimable
» In house
Course Objectives:
This training workshop focuses on the three most fundamental aspects in the design of all machine vision systems, namely lighting, optics and cameras. Participants will learn how to exploit existing constraints and impose new ones in order to simplify the subsequent image processing algorithms, particularly segmentation and feature extraction.
They will also learn the various types of application-specific lighting methods, lens selection, and camera selection through activities and case studies of real-life problems, such as detection of bent lead in a leadframe, inspection of print on packaging, inspection of fast-moving bearing on a conveyor etc.
Machine Vision (Image Processing Fundamentals)
Session Details
» HRDF/HRDC claimable
» In house
Course Objectives:
In this two-day workshop, participants will be guided into understanding how images are processed for the purpose of enhancement, segmentation, feature extraction, defect identification and localization. Basic methods of contrast enhancement, gamma correction, histogram equalization, noise removal by various types of filtering operations, image sharpening, binary and grayscale morphological operations, edge detection using gradient and Laplacian operators, line detection, as well as image segmentation will be demonstrated using Python and OpenCV though hands-on guided activities.
Machine Vision (3-D Vision Fundamentals)
Session Details
» HRDF/HRDC claimable
» In house
Course Objectives:
In this two-day training workshop, the participants will learn about the fundamentals of 3-D machine vision through intensive guided activities and hands-on practical. The most commonly used 3-D methods in machine vision will be covered in this training. These include 3-D stereo imaging, photogrammetry, laser displacement scanning, triangulation, structured lighting method, phase-shift fringe projection method, shadow moiré topography, and phase-measuring deflectometry method. Participants will also be guided into writing OpenCV-Python codes for reconstructing 3-D surface data from phase-shifted fringe pattern images.
