Machine vision and embedded vision systems both fulfill important roles in industry, especially in process control and automation. The difference between the two lies primarily in image processing ...

OpenMV embedded vision goes practical in EEI #57 with Kwabena Agyeman, covering real MCU trade-offs, pipelines, tooling, and ...

Deep learning techniques such as convolutional neural networks (CNN) have significantly increased the accuracy—and therefore the adoption rate—of embedded vision for embedded systems. Starting with ...

The Xilinx Kria K26 targets AO vision applications in smart cities and smart factories. The first product in the company’s new portfolio of SOMs is the Kria K26 SOM, specifically targeting vision AI ...

Embedded Vision and Inferencing are two critical technologies for many modern devices such as drones, autonomous cars, industrial robots, etc. Embedded vision uses computer vision to process images, ...

Imaging technologies such as x-rays and MRI have long been critical diagnostic tools used by healthcare professionals. But it's ultimately up to a human operator to analyze and interpret the images ...

A pharmaceutical packaging line uses vision-guided robots to quickly pick syringes from conveyer belts and place them into packages. Source: Embedded Vision Alliance There’s always been a great deal ...

In Part 1 of this two-part series put together by Embedded Vision Alliance editor-in-chief Brian Dipert and his colleagues Eric Gregori and Shehrzad Qureshi at BDTI, we look at examples of embedded ...

Neural networks have propelled embedded vision to the point where they can be incorporated into low-cost and low-power devices. Embedded vision is becoming a topic of heated conversation thanks to the ...

The National Safety Council reports that 2016 was the deadliest year on US roads in a decade. Autonomous vehicles could eliminate the estimated 90 percent of crashes that are caused by human error, ...