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Now available—the Embedded Vision Summit On-Demand Edition! Gain valuable computer vision and edge AI insights and know-how from the experts at the 2021 Summit.

Face Recognition Functions

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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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Deep Learning Use Cases for Computer Vision (Download)

Six Deep Learning-Enabled Vision Applications in Digital Media, Healthcare, Agriculture, Retail, Manufacturing, and Other Industries The enterprise applications for deep learning have only scratched the surface of their potential applicability and use cases.  Because it is data agnostic, deep learning is poised to be used in almost every enterprise vertical… Deep Learning Use Cases for

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Using Convolutional Neural Networks for Image Recognition

This article was originally published at Cadence's website. It is reprinted here with the permission of Cadence. Convolutional neural networks (CNNs) are widely used in pattern- and image-recognition problems as they have a number of advantages compared to other techniques. This white paper covers the basics of CNNs including a description of the various layers

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Practical Computer Vision Enables Digital Signage with Audience Perception

This article was originally published at Information Display Magazine. It is reprinted here with the permission of the Society of Information Display. Signs that see and understand the actions and characteristics of individuals in front of them can deliver numerous benefits to advertisers and viewers alike.  Such capabilities were once only practical in research labs

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Smart In-Vehicle Cameras Increase Driver and Passenger Safety

This article was originally published at John Day's Automotive Electronics News. It is reprinted here with the permission of JHDay Communications. Cameras located in a vehicle's interior, coupled with cost-effective and power-efficient processors, can deliver abundant benefits to drivers and passengers alike. By Brian Dipert Editor-in-Chief Embedded Vision Alliance Tom Wilson Vice President, Business Development

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Accelerate Machine Learning with the cuDNN Deep Neural Network Library

This article was originally published at NVIDIA's developer blog. It is reprinted here with the permission of NVIDIA. By Larry Brown Solution Architect, NVIDIA Machine Learning (ML) has its origins in the field of Artificial Intelligence, which started out decades ago with the lofty goals of creating a computer that could do any work a

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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

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October 2013 Embedded Vision Summit Technical Presentation: “Better Image Understanding Through Better Sensor Understanding,” Michael Tusch, Apical

Michael Tusch, Founder and CEO of Apical Imaging, presents the "Better Image Understanding Through Better Sensor Understanding" tutorial within the "Front-End Image Processing for Vision Applications" technical session at the October 2013 Embedded Vision Summit East. One of the main barriers to widespread use of embedded vision is its reliability. For example, systems which detect

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September 2013 Qualcomm UPLINQ Conference Presentation: “Accelerating Computer Vision Applications with the Hexagon DSP,” Eric Gregori, BDTI

Eric Gregori, Senior Software Engineer at BDTI, presents the "Accelerating Computer Vision Applications with the Hexagon DSP" tutorial at the September 2013 Qualcomm UPLINQ Conference. Smartphones, tablets and embedded systems increasingly use sophisticated vision algorithms to deliver capabilities like augmented reality and gesture user interfaces. Since vision algorithms are computationally demanding, a key challenge when

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Face Recognition: Learn About GPU Acceleration

Professor Brian Lovell of the University of Queensland, Australia, who's also Chief Technical Officer at Imagus Technology, is a well-known figure in the fields of fields of computer vision and pattern recognition. Lovell is also a long-time advisor to (and advocate of) the Embedded Vision Alliance. On Tuesday November 5 at 9AM PT, Lovell and

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Embedded Vision on Mobile Devices: Opportunities and Challenges

by Tom Wilson CogniVue Brian Dipert Embedded Vision Alliance This article was originally published at Electronic Engineering Journal. It is reprinted here with the permission of TechFocus Media. Courtesy of service provider subsidies coupled with high shipment volumes, relatively inexpensive smartphones and tablets supply formidable processing capabilities: multi-core GHz-plus CPUs and graphics processors, on-chip DSPs

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Microsoft Kinect For Windows 2.0: Developer Registration Is A “Go”

For those of you who haven't already heard, Microsoft unveiled its next-generation Xbox One game console in late May, containing a bundled next-generation "Kinect 2.0" peripheral. Whereas the first-generation Kinect employs a structured light approach to 3-D sensing, "Kinect 2.0" leverages a time-of-flight technique courtesy of Microsoft's 2010 acquisition of Canesta. The included image sensor

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“Machine Learning,” a Presentation from UT Austin

Professor Kristen Grauman of the University of Texas at Austin presents the keynote on machine learning at the December 2012 Embedded Vision Alliance Member Summit. Grauman is a rising star in computer vision research. Among other distinctions, she was recently recognized with a Regents' Outstanding Teaching Award and, along with Devi Parikh, received the prestigious

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December 2012 Embedded Vision Alliance Member Summit Technology Trends Presentation

Embedded Vision Alliance Editor-in-Chief (and BDTI Senior Analyst) Brian Dipert and BDTI Senior Software Engineer Eric Gregori co-deliver an embedded vision application technology trends presentation at the December 2012 Embedded Vision Alliance Member Summit. Brian and Eric discuss embedded vision opportunities in mobile electronics devices. They quantify the market sizes and trends for smartphones and

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ID’ing The Intoxicated: Embedded Vision Picks Out The Inebriated

Speaking of infrared imaging…researchers Georgia Koukiou and Vassilis Anastassopoulos of the University of Patras in Greece recently published (in the International Journal of Electronic Security and Digital Forensics) the results of a study of 20 volunteers to test sobriety (or not) by means of computer algorithms. By doing infrared scans of the volunteers' faces both

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