Software

“Deploying Embedded Vision for Retail Analytics,” a Presentation from RetailNext

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RetailNext's Mark Jamtgaard, Director of Technology, and Bill Adamec, Research and Development Manager, deliver the presentation "Deploying Embedded Vision for Retail Analytics" at the December 2016 Embedded Vision Alliance Member Meeting. Jamtgaard and Adamec Chehebar explain how retailers are using embedded vision to optimize store layout and staffing based on measured customer behavior at scale.

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“New Standards for Embedded Vision and Neural Networks,” a Presentation from the Khronos Group

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Peter McGuinness, representing the Khronos Group, delivers the presentation "New Standards for Embedded Vision and Neural Networks" at the December 2016 Embedded Vision Alliance Member Meeting. McGuinness discusses new standardization work for embedded neural network and vision software.

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“Using Vision to Improve Waste Collection Efficiency,” a Presentation from Compology

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Ben Chehebar, co-founder of Compology, delivers the presentation "Using Vision to Improve Waste Collection Efficiency" at the December 2016 Embedded Vision Alliance Member Meeting. Chehebar describes a novel vision-based solution that is dramatically improving the efficiency of trash collection.

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Facial Analysis Delivers Diverse Vision Processing Capabilities

Algorithms, Articles, Automotive, Edge AI and Vision Alliance, Entertainment, Face Detection, Face Recognition, Medical, NXP Semiconductors, Processors, Retail, Security, Sensors and Cameras, Software, Synopsys, Technical Articles, Tools, Xperi /

Computers can learn a lot about a person from their face – even if they don’t uniquely identify that person. Assessments of age range, gender, ethnicity, gaze direction, attention span, emotional state and other attributes are all now possible at real-time speeds, via advanced algorithms running on cost-effective hardware. This article provides an overview of

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Deep Learning with INT8 Optimization on Xilinx Devices

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This is a reprint of a Xilinx-published white paper which is also available here (1 MB PDF). Xilinx INT8 optimization provide the best performance and most power efficient computational techniques for deep learning inference. Xilinx's integrated DSP architecture can achieve 1.75X solution-level performance at INT8 deep learning operations than other FPGA DSP architectures. ABSTRACT The

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“Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks,” a Presentation from MIT

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Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning. Followup: per Professor Sze, "Slides available at: http://www.rle.mit.edu/eems/wp-content/uploads/2016/07/Sze-Energy-Efficient-Hardware-for-Embedded-Vision-and-Deep-Learning-CVPR-2016-EVW.pdf".

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“Enabling Efficient Heterogeneous Processing Through Coherency,” a Presentation from the HSA Foundation

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Dr. John Glossner, President of the HSA Foundation and CEO of GPT-US, delivers the presentation "Enabling Efficient Heterogeneous Processing Through Coherency" at the September 2016 Embedded Vision Alliance Member Meeting. Glossner describes the organization's goals and deliverables for enabling heterogeneous programming.

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“What’s Augmented? What’s Reality?,” a Presentation from PTC

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Jay Wright, President and General Manager of Vuforia at PTC, delivers the presentation "What's Augmented? What's Reality?" at the September 2016 Embedded Vision Alliance Member Meeting. Wright provides insights on augmented reality markets, applications and technology.

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“Using Vision to Enable Autonomous Land, Sea and Air Vehicles,” a Keynote Presentation from NASA JPL

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Larry Matthies, senior research scientist at the NASA Jet Propulsion Laboratory, presents the "Using Vision to Enable Autonomous Land, Sea and Air Vehicles" keynote at the May 2016 Embedded Vision Summit. Say you’re an autonomous rover and you’ve just landed on Mars. Vexing questions now confront you: “Where am I and how am I moving?”

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Vision Processing Opportunities in Virtual Reality

Algorithms, Articles, Edge AI and Vision Alliance, Entertainment, Infineon, Intel, onsemi, Processors, Sensors and Cameras, Software, Technical Articles, videantis /

VR (virtual reality) systems are beginning to incorporate practical computer vision techniques, dramatically improving the user experience as well as reducing system cost. This article provides an overview of embedded vision opportunities in virtual reality systems, such as environmental mapping, gesture interface, and eye tracking, along with implementation details. It also introduces an industry alliance

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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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