Edge AI and Vision Alliance

May 2018 Embedded Vision Summit Vision Entrepreneurs’ Panel

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Nik Gagvani, President of CheckVideo, moderates the Vision Entrepreneurs’ Panel at the May 2018 Embedded Vision Summit. Other panelists include László Kishonti, CEO of AImotive; Radha Basu, CEO of iMerit; and Gary Bradski, CTO and Co-founder of Arraiy.com, and CEO and Founder of OpenCV.org. What can we learn from leaders of successful vision-based start-ups? The […]

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“Implementing Image Pyramids Efficiently in Software,” a Presentation from Polymorphic Technologies

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Michael Stewart, Proprietor of Polymorphic Technologies, presents the “Implementing Image Pyramids Efficiently in Software,” tutorial at the May 2018 Embedded Vision Summit. An image pyramid is a series of images, derived from a single original image, wherein each successive image is at a lower resolution than its predecessors. Image pyramids are widely used in computer

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“Reduce Risk in Computer Vision Design: Focus on the User,” a Presentation from Twisthink

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Paul Duckworth, Director of Engineering at Twisthink, presents the “Reduce Risk in Computer Vision Design: Focus on the User” tutorial at the May 2018 Embedded Vision Summit. Companies across a wide range of industries are considering ways to apply computer vision to innovate their products and services. With the vast potential of this exciting technology,

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Embedded Vision Insights: September 18, 2018 Edition

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LETTER FROM THE EDITOR Dear Colleague, Lattice Semiconductor will deliver the free webinar “Architecting Always-On, Context-Aware, On-Device AI Using Flexible Low-power FPGAs” on October 30, 2018 at 9 am Pacific Time, in partnership with the Embedded Vision Alliance. The webinar will be presented by Deepak Boppana, the company’s Senior Director of Marketing, and Gordon Hands,

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“Leveraging Cloud Computer Vision for a Real-time Consumer Product,” a Presentation from Cocoon Cam

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Pavan Kumar, Co-founder and CTO at Cocoon Cam, presents the "Leveraging Cloud Computer Vision for a Real-time Consumer Product" tutorial at the May 2018 Embedded Vision Summit. The capabilities of cloud computing are expanding rapidly. At the same time, cloud computing costs are falling. This makes it increasingly attractive to implement computer vision in the

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“Introduction to LiDAR for Machine Perception,” a Presentation from Deepen AI

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Mohammad Musa, the Founder and CEO of Deepen AI, presents the “Introduction to LiDAR for Machine Perception” tutorial at the May 2018 Embedded Vision Summit. LiDAR sensors use pulsed laser light to construct 3D representations of objects and terrain. Recently, interest in LiDAR has grown, for example for generating high-definition maps required for autonomous vehicles

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“Intelligent Consumer Robots Powering the Smart Home,” a Presentation from iRobot

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Mario Munich, Senior Vice President of Technology at iRobot, presents the “Intelligent Consumer Robots Powering the Smart Home” tutorial at the May 2018 Embedded Vision Summit. The Internet Of Things (IoT) has rapidly developed in the past few years, enabled by affordable electronics components and powerful embedded microprocessors, ubiquitous internet access and WiFi in the

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“Approaches for Energy Efficient Implementation of Deep Neural Networks,” a Presentation from MIT

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Vivienne Sze, Associate Professor at MIT, presents the “Approaches for Energy Efficient Implementation of Deep Neural Networks” tutorial at the May 2018 Embedded Vision Summit. Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in

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“Understanding Automotive Radar: Present and Future,” a Presentation from NXP Semiconductors

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Arunesh Roy, Radar Algorithms Architect at NXP Semiconductors, presents the “Understanding Automotive Radar: Present and Future” tutorial at the May 2018 Embedded Vision Summit. Thanks to its proven, all-weather range detection capability, radar is increasingly used for driver assistance functions such as automatic emergency braking and adaptive cruise control. Radar is considered a crucial sensing

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“Building Efficient CNN Models for Mobile and Embedded Applications,” a Presentation from Facebook

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Peter Vajda, Research Scientist at Facebook, presents the “Building Efficient CNN Models for Mobile and Embedded Applications” tutorial at the May 2018 Embedded Vision Summit. Recent advances in efficient deep learning models have led to many potential applications in mobile and embedded devices. In this talk, Vajda discusses state-of-the-art model architectures, and introduces Facebook’s work

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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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