Edge AI and Vision Alliance

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Embedded Vision Insights: April 24, 2018 Edition

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LETTER FROM THE EDITOR Dear Colleague, This year’s Embedded Vision Summit will feature three immersive Vision Technology Workshops: Khronos Standards for Neural Networks and Embedded Vision, Optimized Inference at the Edge with Intel and Artificial Intelligence from Concept to Implementation from Synopsys. Don’t miss your chance to attend the preeminent event for practical computer vision. […]

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“A New Approach to Mass Transit Security,” a Presentation from Lux Research

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Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "A New Approach to Mass Transit Security" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Bünger presents a revolutionary computer-vision-based methodology for public transit safety.

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“Evaluating Early Stage Investment Opportunities in Camera-First Innovations,” a Presentation from Shasta Ventures

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Jacob Mullins, Partner at Shasta Ventures, delivers the presentation "Evaluating Early Stage Investment Opportunities in Camera-First Innovations" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Mullins explains why his firm views the camera as an increasingly central link between the physical and digital worlds.

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“Computer Vision at the Edge and in the Cloud: Architectures, Algorithms, Processors, and Tools,” a Presentation from the Embedded Vision Alliance

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On April 11, 2018, Embedded Vision Alliance founder Jeff Bier delivered the presentation “Computer Vision at the Edge and in the Cloud: Architectures, Algorithms, Processors, and Tools” to the Santa Clara Valley Chapter of the IEEE Signal Processing Society. Bier discussed the benefits and trade-offs of edge, cloud, and hybrid… “Computer Vision at the Edge

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Embedded Vision Insights: April 10, 2018 Edition

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LETTER FROM THE EDITOR Dear Colleague, Time is running out! Today is the last day to save 15% on your 2018 Embedded Vision Summit registration pass with our Early Bird Discount. Don’t miss the chance to accelerate your learning curve and uncover practical techniques in computer vision by attending some of our 80+ “how-to” sessions.

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“Instrumenting Greenhouses as Data-driven Manufacturing Facilities,” a Presentation from IUNU

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Matt King, Chief Technology Officer at IUNU, delivers the presentation "Instrumenting Greenhouses as Data-driven Manufacturing Facilities" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. King explains how his company is enabling increased efficiency in commercial greenhouses using robotic cameras, computer vision and machine learning.

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“The Caffe2 Framework for Mobile and Embedded Deep Learning,” a Presentation from Facebook

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Fei Sun, software engineer at Facebook, delivers the presentation "The Caffe2 Framework for Mobile and Embedded Deep Learning" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Sun introduces Caffe2, a new open-source machine learning framework, and explains how Facebook is using it to enable computer vision in mobile and embedded devices.

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Embedded Vision Insights: March 27, 2018 Edition

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LETTER FROM THE EDITOR Dear Colleague, Keeping up with the rapid pace of vision technology advances can be overwhelming. Accelerate your learning curve and uncover practical techniques in computer vision by attending some of our 80+ "how-to" sessions at the 2018 Embedded Vision Summit, taking place May 21-24 in Santa Clara, California. You'll learn about

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Embedded Vision Insights: March 13, 2018 Edition

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LETTER FROM THE EDITOR Dear Colleague, We’ve got a lot of exciting things lined up for the 2018 Embedded Vision Summit, newly expanded to a four-day event taking place May 21-24 in Santa Clara, California. From 80+ technical sessions, to 100+ technology demos, to keynote presentations from industry luminaries Dean Kamen and Dr. Takeo Kanade—if

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Data Sets for Machine Learning Model Training

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Deep learning and other machine learning techniques have rapidly become a transformative force in computer vision. Compared to conventional computer vision techniques, machine learning algorithms deliver superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence,

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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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