Deep learning has become a useful tool in the integration and implementation of machine vision systems. Deep learning provides value in certain industrial quality inspection applications including tasks such as defect detection and assembly verification, especially when features to be detected require subjective decisions like those that might be made by human visual inspection. Systems integration of components and software - from application analysis to design and implementation to final validation - remains critical to the reliability and performance and this presentation will discuss new best practices that have evolved which can help ensure project success including solutions that use both analytical (“traditional”) machine vision algorithms along with deep learning.
Principal Vision Systems Architect
David L. Dechow is a globally recognized expert in the integration of machine vision, robotics, and industrial automation technologies. He is the Principal Vision Systems Architect for Integro Technologies Corp., where he performs evaluation and design of complex automated imaging solutions covering a wide range of applications. His career in machine vision integration spans more than thirty-five years.
Mr. Dechow is a recipient of the A3 Automated Imaging Achievement Award honoring industry leaders industrial and/or scientific imaging. He is a member of the A3 Imaging Technology Strategy Board, contributing editor for Vision Systems Design magazine, and technical advisory board member with Saccade Vision Ltd.
He is well known for his many informative technical articles, papers, webinars, conference sessions and classes covering machine vision and related enabling technologies in industrial automation.