Winn Hardin, AIA
Companies are using software to help realize many machine vision software developments such as ultra-high-dynamic-range imaging and self-analyzing algorithms.
Integrators have developed sophisticated robotic machine vision systems comprising numerous cameras mounted on a fixed frame to capture many images of an automotive product for quality assurance purposes.
Machine vision standards bring benefits in image data delivery and compatibility while helping keep soldiers safe as well as reducing the cost of upgrading militaries to the newest technologies.
Vision system integrators are looking to stay a step ahead of emerging technologies such as deep learning and other smart technologies.
Machine vision standards are designed to guarantee component interoperability and it allows component manufacturers to develop better overall products.
Deep learning machine vision software essentially allows machines to learn from data representations instead of task-specific algorithms, which could enhance capabilities on the plant floor far beyond what is currently possible.
Advances in cloud computing have improved the quality of service (QoS) for machine vision enough to the point where it is viable for industrial applications.
As machine vision crests a new wave of visibility across mainstream engineering communities, the exceptional growth of 2017 may one day be seen as a threshold moment—the year when everything changed and the only recalibration necessary might be what the industry expects is possible tomorrow.
Track and trace allows machine vision users to prevent counterfeit products by helping them determine the current and past locations of items and to determine which companies or customers have come into contact with them along the supply chain.
There are two main approaches to pattern matching: those based on correlation, and geometric pattern matching and both approaches rely on first locating a region or regions of a template image to provide reference data.