What you’ll learn:
Applying AI to video-conferencing devices.
What is Super-Resolution image expansion?
The impact of deep-learning networks and specter of generative adversarial networks (GANs).
Video conferencing for virtual meetings, distance learning, or socializing has exploded with the onset of the coronavirus pandemic. Some experts suggest that even after the virus recedes, our reliance on virtual gatherings will remain part of our new normality. If so, the huge bandwidth hunger that ubiquitous video conferencing imposes on the internet—from the core out to the thinnest branches—is here to stay.
Even using modern video codecs, a video conference can be demanding on bandwidth: 1 to 2 Mb/s per participant just to keep those thumbnail images on the screen. And there’s growing evidence that with experience, users become more critical of image quality, longing to see fine details of facial expressions, gestures, and posture that carry so much information in an in-person meeting. This trend limits the ability of apps to use higher compression ratios to reduce bandwidth needs. The fine detail the compression algorithm throws out contains just the cues a skilled negotiator needs most.
CEO Luca Verre named of Top 25 Leaders in Manufacturing by SME
The cover person of this year’s list is Rodolphe Barrere, French-born cofounder and CEO of Potloc...
Synaptics pioneered sensors for touchscreens for PCs and mobile devices. But the San Jose-based hardware company has shifted to where the processing is happening — at the edge of the network.
Just about everyone has experienced the frustration of waiting for a health care appointment or service. That is why one Canadian Technology Accelerator in Boston graduate took matters into their own hands, and with the help of the Consulate, expanded the market for their innovative services that proved not only efficient, but in the past year, extremely timely.
The numbers are staggering. Depending on the source, the forecast number of connected IoT devices in 2021 varies from 20 to 40 billion, all producing immense volumes of data.
Prophesee today announced the release of key open-source software modules (OpenEB) and a set of new Event-Based Machine Learning solutions that are aimed at optimizing ML training and inference for event-based applications, including optical flow and object detection. In addition, the company is offering the industry’s largest HD Event-Based dataset to developers as a free download.
This latest release of the company’s Metavision Intelligence Suite includes also adds an expanded set of development tools and software for designing industrial vision systems that leverage the performance and efficiency of Event-Based Vision. The suite now includes close to 100 algorithms, 67 code samples and 11 use-case specific application modules that accelerate the development process.
The open-source modules of OpenEB are available through Github and allow designers to build custom plugins and ensure compatibility with the Metavision Intelligence Suite for developing event-based systems. It provides a platform for developers to share software components across the machine vision ecosystem.
“We want to set an open technology standard in the machine vision ecosystem that enables new levels of accessibility and interoperability. As the leader and technology pioneer in event-based vision systems, our role is to help proliferate its use and make critical development aids, data and tools more readily available to product developers. Our approach provides the growing ecosystem around event-based technology with a rich open foundation and a strong development framework. This includes extensive and reliable data that we have collected over several years, as well as application modules that leverage our expertise in a variety of specific uses to accelerate the development of customer-specific systems,” said Luca Verre, CEO and co-founder of Prophesee.
rench AI developer Prophesee has released a set of key open-source software modules and a set of tools for event-driven Machine Learning such as optical flow and object detection.
As part of the Metavision Intelligence Suite, the Paris-based company is offering the industry’s largest HD Event-Based dataset called OpenEB to developers as a free download. This helps developers use an event-driven approach to machine learning that is triggered by changes rather that neural network frameworks.
The latest release adds an expanded set of development tools and software for designing industrial vision systems with event-driven machine learning. The suite now includes close to 100 algorithms, 67 code samples and 11 use-case specific application modules that accelerate the development process. The open-source modules of OpenEB are available through Github and allow designers to build custom plugins and ensure compatibility with the Metavision Intelligence Suite for developing event-based systems. It provides a platform for developers to share software components across the machine vision ecosystem.
Prophesee today announced the release of OpenEB, a set of key open-source software modules and a set of new Event-Based Machine Learning solutions. The new products are aimed at optimizing ML training and inference for event-based applications, including optical flow and object detection. In addition, the company is offering the industry’s largest HD Event-Based dataset to developers as a free download.
This latest release of the company’s Metavision® Intelligence Suite includes an expanded set of development tools and software for designing industrial vision systems that leverage the performance and efficiency of Event-Based Vision. The suite now includes close to 100 algorithms, 67 code samples and 11 use-case specific application modules that accelerate the development process.
The open-source modules of OpenEB are available through Github and allow designers to build custom plugins and ensure compatibility with the Metavision Intelligence Suite for developing event-based systems. It also provides a platform for developers to share software components across what they call the “machine vision ecosystem”.
“If you look at topics like AI and how much the US and China are investing in these, Europe is an order of magnitude behind,” says Ingo Ramesohl, managing director of Robert Bosch Venture Capital, one of Europe’s oldest and most successful corporate venture funds.
RBVC is to some extent helping to redress this balance, with investments into companies like Budapest-based self-driving car startup AImotive, and Prophesee, the French neuromorphic vision systems startup, but Europe’s biggest strengths may be in other areas of deeptech, says Ramesohl.
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