Embedded Vision Summit with over 100 Presentations
Since 2012, the Embedded Vision Summit has been the leading conference and trade fair for practical computer vision and AI applications in machine vision. This year’s conference, which will again be held in person in Santa Clara, California, boasts 55 exhibitors and 100 presenters. More than 1,000 machine vision experts are expected to attend.
This year’s conference, which will again be held in person in Santa Clara, California, boasts 55 exhibitors and 100 presenters. More than 1,000 machine vision experts are expected to attend.
The Embedded Vision Summit will take place May 16-19 in Santa Clara, California. More than 100 presentations on embedded vision and deep learning technologies and tools will be offered. In addition to product and technology presentations, case studies and exciting panel discussions will liven up the program. Moreover, 55 exhibitors will present their new developments as well as more than 100 application examples at the accompanying trade exhibition, which will cover all the technical and business aspects of computer vision, deep learning, AI and related technologies.
The Embedded Vision Summit is organized by the Edge AI and Vision Alliance. They expect just over 1,000 product and application developers, business leaders, and users, all focused on artificial intelligence at the edge.
Workshops on AI in the Edge
New in 2022 is the Edge AI Deep Dive Day, a series of workshops focused on specific topics in AI in the edge domain. The keynote speaker for the Embedded Vision Summit 2022 will be Dr. Ryad Benosman, a leading leading expert in the field of neuromorphic sensing and computing. Benosman is a professor at the University of Pittsburgh and an associate professor at the CMU Robotics Institute. In his keynote "Event-based Neuromorphic Perception and Computation: The Future of Sensing and AI", he will explain the fundamentals of bio-inspired, event-based image sensing and processing approaches, explore their strengths and weaknesses, and show that bio-inspired image processing systems have the potential to significantly outperform traditional visual AI approaches.
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