Processors

May 2014 Embedded Vision Summit Technical Presentation: “Fast 3D Object Recognition in Real-World Environments,” Ken Lee, VanGogh Imaging

Ken Lee, Founder of VanGogh Imaging, presents the "Fast 3D Object Recognition in Real-World Environments" tutorial at the May 2014 Embedded Vision Summit. Real-time 3D object recognition can be computationally intensive and difficult to implement when there are a lot of other objects (i.e. clutter) around the target. There are several approaches to deal with […]

May 2014 Embedded Vision Summit Technical Presentation: “Fast 3D Object Recognition in Real-World Environments,” Ken Lee, VanGogh Imaging Read More +

May 2014 Embedded Vision Summit Technical Presentation: “Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking,” Chris Rowen, Cadence

Chris Rowen, Fellow at Cadence, presents the "Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking" tutorial at the May 2014 Embedded Vision Summit. This presentation looks at a cross-section of advanced feature detectors, and considers the algorithm, bit precision, arithmetic primitives and implementation optimizations that yield high pixel processing rates,

May 2014 Embedded Vision Summit Technical Presentation: “Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking,” Chris Rowen, Cadence Read More +

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May 2014 Embedded Vision Summit Proceedings

The Embedded Vision Summit was held on May 29, 2014 in Santa Clara, California, as a technical educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The program for the event included the following presentations, whose PDF-formatted foilsets are available for download as a… May 2014 Embedded Vision Summit

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GPUTech

Embedded Vision: Enabling Smarter Mobile Apps and Devices

For decades, computer vision technology was found mainly in university laboratories and a few niche applications. Today, virtually every tablet and smartphone is capable of sophisticated vision functions such as hand gesture recognition, face recognition, gaze tracking, and object recognition. These capabilities are being used to enable new types of applications, user interfaces, and use

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March 2014 Embedded Vision Alliance Member Meeting Presentation: “Vision-Based Navigation Applications: From Planetary Exploration to Consumer Devices,” Larry Matthies, NASA

Larry Matthies, Supervisor of the Computer Vision Group at NASA's Jet Propulsion Laboratory, delivers the technology presentation, "Vision-Based Navigation Applications: From Planetary Exploration to Consumer Devices," at the March 2014 Embedded Vision Alliance Member Meeting. Dr. Matthies is a Senior Research Scientist at JPL and is the Supervisor of the Computer Vision Group in the

March 2014 Embedded Vision Alliance Member Meeting Presentation: “Vision-Based Navigation Applications: From Planetary Exploration to Consumer Devices,” Larry Matthies, NASA Read More +

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Augmented Reality: A Compelling Mobile Embedded Vision Opportunity

This article was originally published at Electronic Engineering Journal. It is reprinted here with the permission of TechFocus Media. Although augmented reality was first proposed and crudely demonstrated nearly fifty years ago, its implementation was until recently only possible on bulky and expensive computers. Nowadays, however, the fast, low power and cost-effective processors and high

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GPUTech

Real-Time Traffic Sign Recognition on Mobile Processors

There is a growing need for fast and power-efficient computer vision on embedded devices. This session will focus on computer vision capabilities on embedded platforms available to ADAS developers, covering the OpenCV CUDA implementation and the new computer vision standard, OpenVX. In addition, Itseez traffic sign detection will be showcased. The algorithm is capable of

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GPUTech

Getting Started With GPU-Accelerated Computer Vision Using OpenCV and CUDA

OpenCV is a free library for research and commercial purposes that includes hundreds of optimized computer vision and image processing algorithms. NVIDIA and Itseez have optimized many OpenCV functions using CUDA on desktop machines equipped with NVIDIA GPUs. These functions are 5 to 100 times faster in wall-clock time compared to their CPU counterparts. Anatoly

Getting Started With GPU-Accelerated Computer Vision Using OpenCV and CUDA Read More +

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Improved Vision Processors, Sensors Enable Proliferation of New and Enhanced ADAS Functions

This article was originally published at John Day's Automotive Electronics News. It is reprinted here with the permission of JHDay Communications. Thanks to the emergence of increasingly capable and cost-effective processors, image sensors, memories and other semiconductor devices, along with robust algorithms, it's now practical to incorporate computer vision into a wide range of embedded

Improved Vision Processors, Sensors Enable Proliferation of New and Enhanced ADAS Functions Read More +

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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