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In-depth information about the embedded vision market, applications, technologies, products, and trends.

The content in this section of the website comes from Embedded Vision Alliance™ members and other industry luminaries. The Overview shows all of the content, sorted by date. Other tabs sort the material by type. If you'd like to contribute an original, in-depth article to the Alliance, propose your content in this section of the forum.

The substantial parallel processing resources in modern GPUs makes them a natural choice for implementing vision-processing functions.


The level of "smart camera" intelligence has increased significantly over time. Today's cameras are driven by computer vision technology.

Yury Gorbachev of Itseez delivers a technical presentation at the May 2016 Embedded Vision Summit.


CDNN2 enables localized, deep learning-based video analytics on camera devices in real time, and adds support for Google's TensorFlow.

Dr. Chris Rowen of Cadence delivers a business presentation at the May 2016 Embedded Vision Summit.


Instead of being vetted and improved in a sporadic crowd-sourced manner, formal industry standards have a structured process behind them.

It was clear at the annual Embedded Vision Summit that the time of computer vision and deep learning on mobile device had finally arrived.

Pete Warden of Google delivers an enabling technologies presentation at the May 2016 Embedded Vision Summit.


Integrating an embedded video stabilization solution into the imaging pipeline of a product adds significant value to the customer.

The Embedded Vision Alliance's June 21, 2016 email newsletter edition covers a diversity of embedded vision technology and product topics.