Machine Vision Training Workshops

The pre-processing of digital images and extraction of useful features from the images for the purpose of decision making, sorting and classification are crucial for the successful implementation of machine vision solutions in the industry. 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.

The training workshop is divided into activity-based theory (70%) to strengthen the fundamental knowledge of the participants, and practical activities based on digital processing of captured images (30%). The activities will be based on practical applications of image processing, such as detection and localization of stamping defects in a lead frame, detection of soldering defects, wire bond defects, wafer imprinting defects, blister pack inspection etc. The practical part will include detection of missing components on a printed circuit board, and blister pack inspection for missing capsules.

2-D vision systems are able to provide high-speed, consistent and accurate product inspection in the industry. However, they are not capable of detecting defects that manifest as height variation on the surface of the products, such as dents and protrusions. Neither are they able to measure heights and 3-D surface profiles of products. 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.

The training workshop is divided into activity-based theory (60%) to strengthen the fundamental knowledge of the participants on the underlying technologies of 3-D surface measurement, and hands-on practical activities (40%). The practical activities involve capturing 2-D images of smooth surfaces and surfaces with step heights using special lighting methods, and reconstructing 3-D surface data from the 2-D images.