Edge AI and Vision Alliance

LETTER FROM THE EDITOR Dear Colleague, Last week, the Edge AI and Vision Alliance announced the winners of the 2020 Vision Product of the Year Awards. The Awards recognize the innovation and excellence of the industry’s leading technology companies that are enabling practical visual AI and computer vision.This year’s winners included NVIDIA’s Jetson Nano (Best […]

LETTER FROM THE EDITOR Dear Colleague, Join us at the 2020 Vision Product of the Year Awards Ceremony, a free online event next Wednesday, June 3, 2020 from 9-10 am PT. We will announce the five winning companies (selected by our panel of independent judges) that are developing the next generation of computer vision products

LETTER FROM THE EDITOR Dear Colleague, The Vision Tank is the Edge AI and Vision Alliance’s annual start-up competition, showcasing the best new ventures using computer vision or visual AI in their products and services. It’s open to early-stage companies, and entrants are judged on four criteria: technology innovation, business plan, team and business opportunity.

LETTER FROM THE EDITOR Dear Colleague, We’ve got big news! Originally scheduled to take place in person next month in California, the 2020 Embedded Vision Summit is moving to a fully online experience. The event will be made up of five sessions taking place Tuesdays and Thursdays from September 10 through September 24 from 9

LETTER FROM THE EDITOR Dear Colleague, Today hundreds of companies are developing embedded vision and visual AI building-block technologies—such as processors, algorithms and camera modules—and thousands of companies are creating systems and solutions incorporating these technologies. With so many companies in the space, and new companies entering constantly, it has become difficult to find the

MEDICAL APPLICATIONS Deep Learning for Medical Imaging: COVID-19 (Novel Coronavirus) Detection In this guest blog post published by MathWorks, Dr. Barath Narayanan from the University of Dayton Research Institute, along with his colleague Dr. Russell C. Hardie from the University of Dayton, provide a detailed description of how to implement a deep learning-based technique for