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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 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.


Elif Albuz of NVIDIA delivers a presentation at the December 2014 Embedded Vision Alliance Member Meeting.

Doug Johnston of Prism Skylabs delivers a presentation at the December 2014 Embedded Vision Alliance Member Meeting.

Innovative semiconductor architectures are emerging to provide powerful, low-power image capture and knowledge extraction capabilities.

Vision is rapidly emerging in the automotive market and, fused with other sensor technologies, is improving the safety of vehicles.

Signs that see and understand the actions and characteristics of individuals in front of them can deliver numerous benefits.

This is Appendix D of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

This is Appendix C of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

Linley Gwennap of the Linley Group delivers a presentation at the September 2014 Embedded Vision Alliance Member Meeting.

This is Appendix B of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

Dr. Ren Wu of Baidu delivers a presentation at the September 2014 Embedded Vision Alliance Member Meeting.

Mel Spiese of Cubic delivers a presentation at the September 2014 Embedded Vision Alliance Member Meeting.

Scott Krig, the author of "Computer Vision Metrics," delivers a presentation at the September 2014 Embedded Vision Alliance Member Meeting.

Matt Bell of Matterport delivers a presentation at the September 2014 Embedded Vision Alliance Member Meeting.

This is Part B of Appendix A of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

This is Part A of Appendix A of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

Cameras located in a vehicle's interior, coupled with cost-effective and power-efficient processors, can deliver abundant occupant benefits.

Computer Vision Metrics: Chapter Eight (Part F)

This is Part E of Chapter 8 of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

This is Part D of Chapter 8 of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.

This is Part C of Chapter 8 of "Computer Vision Metrics: Survey, Taxonomy, and Analysis," written by Scott Krig and published by Apress.