Edge AI and Vision Alliance

“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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“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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Embedded Vision Insights: September 6, 2018 Edition

LETTER FROM THE EDITOR Dear Colleague, The next session of the Embedded Vision Alliance's in-person, hands-on technical training class series, Deep Learning for Computer Vision with TensorFlow, takes place in less than a month in San Jose, California. These classes give you the critical knowledge you need to develop deep learning computer vision applications with

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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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“The Role of the Cloud in Autonomous Vehicle Vision Processing: A View from the Edge,” a Presentation from NXP Semiconductors

Ali Osman Ors, Director of Automotive Microcontrollers and Processors at NXP Semiconductors, presents the “Role of the Cloud in Autonomous Vehicle Vision Processing: A View from the Edge” tutorial at the May 2018 Embedded Vision Summit. Regardless of the processing topology—distributed, centralized or hybrid —sensor processing in automotive is an edge compute problem. However, with

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Embedded Vision Insights: August 21, 2018 Edition

VISION PROCESSING IN THE CLOUD Introduction to Creating a Vision Solution in the Cloud A growing number of applications utilize cloud computing for execution of computer vision algorithms. In this presentation, Nishita Sant, Computer Vision Scientist at GumGum, introduces the basics of creating a cloud-based vision service, based on GumGum's experience developing and deploying a

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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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