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Processors

May 2019 Embedded Vision Summit Slides

The Embedded Vision Summit was held on May 20-23, 2019 in Santa Clara, California, as an educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations delivered at the Summit are listed below. All of the slides from these presentations are included in… May 2019 Embedded Vision Summit […]

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“Computer Vision Developer Survey,” a March 2019 Silicon Valley Meetup Presentation from the Embedded Vision Alliance

Jeff Bier, Founder of the Embedded Vision Alliance, delivers the presentation "Computer Vision Developer Survey," at the Embedded Vision Alliance's March 2019 Silicon Valley Meetup. Bier shares findings from the Alliance’s recent survey of developers about the software, processors and tools they use to build computer vision and visual AI products.

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Speeding Up Semantic Segmentation Using MATLAB Container from NVIDIA NGC

This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. Gone are the days of using a single GPU to train a deep learning model.  With computationally intensive algorithms such as semantic segmentation, a single GPU can take days to optimize a model. But multi-GPU hardware is expensive,

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“Computer Vision Developer Survey,” a March 2019 Silicon Valley Meetup Presentation from the Embedded Vision Alliance (PDF)

The Embedded Vision Alliance hosted a Computer Vision and Visual AI Meetup on the evening of March 13, 2019 in San Jose, California. Speakers included: Satya Mallick, Interim CEO of OpenCV.Org, who provided an overview of the OpenCV deep learning module and a preview of planned improvements to it Bing… “Computer Vision Developer Survey,” a

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“SoCs for Computer Vision-enabled IoT Devices,” a March 2019 Silicon Valley Meetup Presentation from MediaTek (PDF)

The Embedded Vision Alliance hosted a Computer Vision and Visual AI Meetup on the evening of March 13, 2019 in San Jose, California. Speakers included: Satya Mallick, Interim CEO of OpenCV.Org, who provided an overview of the OpenCV deep learning module and a preview of planned improvements to it Bing… “SoCs for Computer Vision-enabled IoT

“SoCs for Computer Vision-enabled IoT Devices,” a March 2019 Silicon Valley Meetup Presentation from MediaTek (PDF) Read More +

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“Deep Learning with OpenCV,” a March 2019 Silicon Valley Meetup Presentation from OpenCV.org

The Embedded Vision Alliance hosted a Computer Vision and Visual AI Meetup on the evening of March 13, 2019 in San Jose, California. Speakers included: Satya Mallick, Interim CEO of OpenCV.Org, who provided an overview of the OpenCV deep learning module and a preview of planned improvements to it Bing… “Deep Learning with OpenCV,” a

“Deep Learning with OpenCV,” a March 2019 Silicon Valley Meetup Presentation from OpenCV.org Read More +

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Harnessing the Power of AI: An Easy Start with Lattice’s sensAI

This article was originally published at Lattice Semiconductor’s website. It is reprinted here with the permission of Lattice Semiconductor. Artificial intelligence, or AI, is everywhere. It’s a revolutionary technology that is slowly pervading more industries than you can imagine. It seems that every company, no matter what their business, needs to have some kind of

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“Tools and Processors for Computer Vision,” Selected Results from the Embedded Vision Alliance’s January 2019 Computer Vision Developer Survey

Since 2015, the Embedded Vision Alliance has surveyed computer vision developers regarding the products they are working on and the hardware and software tools they are using in their projects. This white paper provides selected results from our most recent survey, conducted in November 2018. We received responses from 692… “Tools and Processors for Computer

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Combining an ISP and Vision Processor to Implement Computer Vision

An ISP (image signal processor) in combination with one or several vision processors can collaboratively deliver more robust computer vision processing capabilities than vision processing is capable of providing standalone. However, an ISP operating in a computer vision-optimized configuration may differ from one functioning under the historical assumption that its outputs would be intended for

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Multi-sensor Fusion for Robust Device Autonomy

While visible light image sensors may be the baseline “one sensor to rule them all” included in all autonomous system designs, they’re not necessarily a sole panacea. By combining them with other sensor technologies: “Situational awareness” sensors; standard and high-resolution radar, LiDAR, infrared and UV, ultrasound and sonar, etc., and “Positional awareness” sensors such as

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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