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“The OpenCV Open Source Computer Vision Library: What’s New and What’s Coming?,” a Presentation from the OpenCV Foundation

Gary Bradski, President and CEO of the OpenCV Foundation, presents the "The OpenCV Open Source Computer Vision Library: What’s New and What’s Coming?" tutorial at the May 2016 Embedded Vision Summit. OpenCV is an enormously popular open source computer vision library, with over 14 million downloads expanding recently to 200K downloads per month. Originally used […]

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“Democratizing Computer Vision Development: Lessons from the Video Game Industry,” a Presentation from WRNCH

Paul Kruszewski, President of WRNCH, presents the "Democratizing Computer Vision Development: Lessons from the Video Game Industry" tutorial at the May 2016 Embedded Vision Summit. Computer vision offers great promise: algorithms are maturing rapidly and processing power continues to grow by leaps and bounds. But today’s approach to computer vision software development – hiring a

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“Computer Vision 2.0: Where We Are and Where We’re Going,” a Presentation from the Embedded Vision Alliance

Jeff Bier, founder of the Embedded Vision Alliance, presents the "Computer Vision 2.0: Where We Are and Where We're Going" plenary session at the May 2016 Embedded Vision Summit. Computer vision has rapidly transitioned from a research topic with few commercial applications to a mainstream technology with applications in virtually every sector of our economy.

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“Computational Photography: Understanding and Expanding the Capabilities of Standard Cameras,” a Presentation from NVIDIA

Orazio Gallo, Senior Research Scientist at NVIDIA, presents the "Computational Photography: Understanding and Expanding the Capabilities of Standard Cameras" tutorial at the May 2016 Embedded Vision Summit. Today's digital cameras, even at the entry-level, produce pictures with quality comparable to that of high-end cameras of a decade ago. Image processing and computational photography algorithms play

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“Large-Scale Deep Learning for Building Intelligent Computer Systems,” a Keynote Presentation from Google

Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit. Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that

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May 2016 Embedded Vision Summit Proceedings

The Embedded Vision Summit was held on May 2-4, 2016 in Santa Clara, California, as a educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations presented at the Summit are listed below. All of the slides from these presentations are included in… May 2016 Embedded Vision Summit

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OpenVX Enables Portable, Efficient Vision Software

OpenVX, a maturing API from the Khronos Group, enables embedded vision application software developers to efficiently harness the various processing resources available in SoCs and systems. Vision technology is now enabling a wide range of products, that are more intelligent and responsive than before, and thus more valuable to users. Such image perception, understanding, and

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“Deep Learning from a Mobile Perspective,” a Presentation from Caffe Developer Yangqing Jia

Yangqing Jia created the Caffe framework while a graduate student researcher at UC Berkeley. He later was a member of the Google Brain project and recently joined Facebook, working on various aspects of deep learning research and engineering. At the Alliance’s February 2016 tutorial on deep learning for computer vision using convolutional neural networks and

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“An Update on Open Standard APIs for Vision Processing,” a Presentation from Khronos

Neil Trevett, President of Khronos and Vice President at NVIDIA, delivers the presentation, "Update on Khronos Open Standard APIs for Vision Processing," at the December 2015 Embedded Vision Alliance Member Meeting. Trevett provides an update on recent developments in multiple Khronos standards useful for vision applications.

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Accelerating Machine Learning: Implementing Deep Neural Networks on FPGAs

This introductory article discusses implementing machine learning algorithms on FPGAs, achieving significant performance improvements at much lower power. Newly available middleware IP, together with the SDAccel programming environment, enables software developers to implement convolutional neural networks (CNNs) in C/C++, leveraging an OpenCL platform model. Machine Learning in the Cloud: A Tipping Point The transformation of

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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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