Videos

Cadence Demonstration of the First Vision DSP Compliant with OpenVX 1.1

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Megha Dagha, Senior Technical Marketing Manager at Cadence, demonstrates the company's latest embedded vision technologies and products at the May 2017 Embedded Vision Summit. Specifically, Daga demonstrates an edge-detection algorithm optimally implemented using OpenVX on a dual-core system, using an Xtensa® processor as the host and a Vision processor as the DSP. OpenVX is a […]

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“Collaboratively Benchmarking and Optimizing Deep Learning Implementations,” a Presentation from General Motors

Algorithms & Models, Automotive, Edge AI and Vision Alliance, Processors, Software, Tools, Videos /

Unmesh Bordoloi, Senior Researcher at General Motors, presents the "Collaboratively Benchmarking and Optimizing Deep Learning Implementations" tutorial at the May 2017 Embedded Vision Summit. For car manufacturers and other OEMs, selecting the right processors to run deep learning inference for embedded vision applications is a critical but daunting task.  One challenge is the vast number

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“New Dataflow Architecture for Machine Learning,” a Presentation from Wave Computing

Algorithms & Models, Edge AI and Vision Alliance, Processors, Software, Tools, Videos, Wave Computing /

Chris Nicol, CTO at Wave Computing, presents the "New Dataflow Architecture for Machine Learning" tutorial at the May 2017 Embedded Vision Summit. Data scientists have made tremendous advances in the use of deep neural networks (DNNs) to enhance business models and service offerings. But training DNNs can take a week or more using traditional hardware

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“How to Test and Validate an Automated Driving System,” a Presentation from MathWorks

Algorithms & Models, Information Access and Analytics, MathWorks, Object Identification, Object Tracking, Optical Character Recognition, Processors, Software, Tools, Videos /

Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from

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BDTI Demonstration of Computer Vision on Low-Cost Processors

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Jeremy Giddings, Director of Business Development at BDTI, demonstrates the company’s capabilities for creating efficient implementations of computer vision algorithms at the May 2017 Embedded Vision Summit. Specifically, Giddings demonstrates face detection running on a low-cost Renesas RZG family processor. The face detection algorithm was implemented by BDTI to run in real time with minimal

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BDTI Demonstration of Skillful Restructuring of 3D Sensing Algorithms

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Jeremy Giddings, Director of Business Development at BDTI, demonstrates the company's capabilities for creating efficient implementations of computer vision algorithms at the May 2017 Embedded Vision Summit. Specifically, Giddings demonstrates Tango, Google’s 3D sensing technology, running on the Lenovo Phab 2 Pro smartphone, the first production device with this technology. This successful product was the

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BDTI Demonstration of Neural Network-Based Object Detection

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Jeremy Giddings, Director of Business Development at BDTI, demonstrates the company's machine learning engineering services at the May 2017 Embedded Vision Summit. Specifically, Giddings demonstrates BDTI’s ability to design, train, and implement neural networks for specific use cases. For this demo, BDTI engineers implemented YOLO (You Only Look Once), an approach in which classification and

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“Enabling the Full Potential of Machine Learning,” a Presentation from Wave Computing

Algorithms & Models, Edge AI and Vision Alliance, Processors, Software, Tools, Videos, Wave Computing /

Derek Meyer, CEO of Wave Computing, presents the "Enabling the Full Potential of Machine Learning" tutorial at the May 2017 Embedded Vision Summit. With the growing recognition that “data is the new oil,” more companies are looking to machine learning to gain competitive advantages and create new business models. But the machine learning industry is

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“Approaches for Vision-based Driver Monitoring,” a Presentation from PathPartner Technology

Algorithms & Models, Automotive, Face Detection, PathPartner Technology, Processors, Sensors and Cameras, Software, Tools, Videos /

Jayachandra Dakala, Technical Architect at PathPartner Technology, presents the "Approaches for Vision-based Driver Monitoring" tutorial at the May 2017 Embedded Vision Summit. Since many road accidents are caused by driver inattention, assessing driver attention is important to preventing accidents. Distraction caused by other activities and sleepiness due to fatigue are the main causes of driver

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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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