Automating Visual Inspection with AI and Image Processing
Overview
Visual Inspection is rapidly becoming a critical component of engineering systems today. AI and machine vision are being applied to design and develop novel methods, including defect detection, measurement, and text/bar code identification in images and video. In this session, we will explore the latest advancements and practical applications of visual inspection technologies in various engineering domains.
Highlights
- Industry examples of automated visual inspection for inline and offline manufacturing systems
- Image acquisition considerations for collecting good data
- Traditional image processing techniques to perform anomaly detection via segmentation and blob analysis
- AI-based workflows for object detection, classification, and anomaly detection
- Deployment of visual inspection algorithms to standalone apps, embedded devices, and enterprise systems
Who Should Attend
Engineers and project managers involved in manufacturing, quality assurance, and process automation.
About the Presenter
Sharon Kim is an Application Engineer at MathWorks in Santa Clara, CA. She specializes in supporting the image processing, signals processing and deep learning application areas. Sharon holds a B.S. in Biomedical Engineering from Johns Hopkins, and an M.S. and Ph.D. in Biomedical Engineering from Columbia University, where she conducted image and signal processing, machine learning, and data visualization on multi-spectral wide-field and microscopy images to study resting state brain activity.
