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

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

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

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

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

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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“Hybrid Semi-Parallel Deep Neural Networks (SPDNN) – Example Methodologies & Use Cases,” a Presentation from Xperi

Peter Corcoran, co-founder of FotoNation (now a core business unit of Xperi) and lead principle investigator and director of C3Imaging (a research partnership between Xperi and the National University of Ireland, Galway), presents the “Hybrid Semi-Parallel Deep Neural Networks (SPDNN) – Example Methodologies & Use Cases” tutorial at the May 2018 Embedded Vision Summit. Deep

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

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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“Harnessing the Edge and the Cloud Together for Visual AI,” a Presentation from Au-Zone Technologies

Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, presents the “Harnessing the Edge and the Cloud Together for Visual AI” tutorial at the May 2018 Embedded Vision Summit. Embedded developers are increasingly comfortable deploying trained neural networks as static elements in edge devices, as well as using cloud-based vision services to implement visual intelligence remotely.

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“Improving and Implementing Traditional Computer Vision Algorithms Using DNN Techniques,” a Presentation from Imagination Technologies

Paul Brasnett, Senior Research Manager for Vision and AI in the PowerVR Division at Imagination Technologies, presents the “Improving and Implementing Traditional Computer Vision Algorithms Using DNN Techniques” tutorial at the May 2018 Embedded Vision Summit. There has been a very significant shift in the computer vision industry over the past few years, from traditional

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“Architecting a Smart Home Monitoring System with Millions of Cameras,” a Presentation from Comcast

Hongcheng Wang, Senior Manager of Technical R&D at Comcast, presents the “Architecting a Smart Home Monitoring System with Millions of Cameras” tutorial at the May 2018 Embedded Vision Summit. Video monitoring is a critical capability for the smart home. With millions of cameras streaming to the cloud, efficient and scalable video analytics becomes essential. To

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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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