Bookmark and Share

Welcome

The Embedded Vision Academy is a free online training facility for embedded vision product developers. This program provides educational and other resources to help engineers integrate visual intelligence―the ability of electronic systems to see and understand their environments―into next-generation embedded and consumer devices.

The goal of the Academy is to make it possible for engineers worldwide to gain the skills needed for embedded vision product and application development. Course material in the Embedded Vision Academy spans a wide range of vision-related subjects, from basic vision algorithms to image pre-processing, image sensor interfaces, and software development techniques and tools such as OpenCV. Courses will incorporate training videos, interviews, demonstrations, downloadable code, and other developer resources―all oriented towards developing embedded vision products.

The Embedded Vision Alliance™ plans to continuously expand the curriculum of the Embedded Vision Academy, so engineers will be able to return to the site on an ongoing basis for new courses and resources. The listing below showcases the most recently published Embedded Vision Academy content. Reference the links on the right side of this page to access the full suite of embedded vision content, sorted by technology, application, function, viewer experience level, provider, and type.

Peter Shannon of Firelake Capital Management delivers a business presentation at the May 2016 Embedded Vision Summit.

This chapter describes the conclusions from the optimization process.

Gary Bradski of the OpenCV Foundation delivers a technical presentation at the May 2016 Embedded Vision Summit.

Paul Kruszewski of WRNCH delivers a business presentation at the May 2016 Embedded Vision Summit.

Marco Jacobs of videantis delivers a business presentation at the May 2016 Embedded Vision Summit.

Jeff Bier of the Embedded Vision Alliance delivers the plenary session presentation at the May 2016 Embedded Vision Summit.

This chapter describes the performance of some common 3 x 3 convolution matrices using the fully optimized code.

Orazio Gallo of NVIDIA delivers a technical presentation at the May 2016 Embedded Vision Summit.

This chapter describes variations to the algorithm for 3 x 3 convolution matrices to create an algorithm for 5 x 5 convolution matrices.

Jeff Dean of Google delivers the Monday keynote presentation at the May 2016 Embedded Vision Summit.