Brian Dipert

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NVIDIA Unveils Palm-Sized, Energy-Efficient AI Computer for Self-Driving Cars

New DRIVE PX 2 to Provide In-Vehicle Driving, Mapping for Baidu's Autonomous Car Monday, September 12, 2016 — GTC China — NVIDIA today unveiled a palm-sized, energy-efficient artificial intelligence (AI) computer that automakers can use to power automated and autonomous vehicles for driving and mapping. The new single-processor configuration of the NVIDIA® DRIVE™ PX 2 […]

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VisionSystemsDesign

Deep Learning for Vision Processing: More In-depth Design Techniques

This blog post was originally published at Vision Systems Design's website. It is reprinted here with the permission of PennWell. Last time, I showcased new videos and articles from the Embedded Vision Alliance that provide tips for those using convolutional neural networks (CNNs) and other deep learning techniques. This time, I'll highlight additional in-depth resources

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Renesas Demonstrations of Surround View, Pedestrian Detection and Sign Recognition

Craig Johnson, Senior Manager at Renesas, demonstrates the company's latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Johnson demonstrates surround view and deep learning-based pedestrian detection and sign recognition, all running on the company's R-Car platform.

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Thales Group Demonstrations of its Research Projects

François Duhem, Research Engineer at Thales Group, demonstrates the organization's latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Duhem discusses the organization's activities in designing power-efficient embedded platforms for real-time image processing applications through two research projects. COPCAMS (www.copcams.eu) develops a solution to give industrial-grade cameras an infinite depth

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EVA180x100

Embedded Vision Insights: September 13, 2016 Edition

LETTER FROM THE EDITOR Dear Colleague, Next Thursday, September 22 from 9 am to 5 pm, the primary Caffe developers from U.C. Berkeley's Vision and Learning Center will present "Deep Learning for Vision Using CNNs and Caffe," a full-day detailed technical tutorial focused on convolutional neural networks (CNNs) for vision and the Caffe framework for

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Renesas Demonstrations of Gaze Tracking and Deep Learning-based Object Recognition

David Olsen, Staff Product Marketing Manager at Renesas, demonstrates the company's latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Olsen demonstrates gaze tracking and deep learning-based object recognition, both running on the company's RZ/G1M microprocessor.

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Almalence Demonstration of SuperResolution

Dan Sakols, Vice President of Business Development at Almalence, demonstrates the company's latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Sakols demonstrates the company's SuperResolution technology, which allows resolution improvement at video frame rates, accelerated on the Qualcomm Hexagon vector processor. SuperResolution on Hexagon permits mobile devices to zoom

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VeriSilicon Demonstration of AlexNet Deep Learning-based Object Recognition

Mahadev Kolluru, Senior Director of Platform Solutions at VeriSilicon, demonstrates the company's latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Kolluru demonstrates AlexNet deep learning-based object recognition running on the company's Vivante VIP Nano vision processing core.

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ZMicro Demonstration of Its Insight Video Enhancement Product

Jason Wade, President of ZMicro, demonstrates the company's latest embedded vision technologies and products at the May 2016 Embedded Vision Summit. Specifically, Wade demonstrates the value of the company's latest video processing system, Insight, which significantly improves visual clarity in real-time video, providing an operator with more information to support better decisions. He discusses the

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