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May 2017 Embedded Vision Summit Slides

The Embedded Vision Summit was held on May 1-3, 2017 in Santa Clara, California, as a educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations delivered at the Summit are listed below. All of the slides from these presentations are included in… May 2017 Embedded Vision Summit […]

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“Combining Cloud and Edge Machine Learning to Deliver the Future of Video Monitoring,” a Presentation from Camio

Carter Maslan and Luca de Alfaro of Camio deliver the presentation "Combining Cloud and Edge Machine Learning to Deliver the Future of Video Monitoring" at the February 2017 Embedded Vision Alliance Member Meeting. Maslan and de Alfaro present their company's approach to using machine learning at the edge and in the cloud to deliver more

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Facial Analysis Delivers Diverse Vision Processing Capabilities

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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“Intelligent Video Surveillance: Are We There Yet?,” a Presentation from CheckVideo

Nik Gagvani, President and General Manager of CheckVideo, delivers the presentation "Intelligent Video Surveillance: Are We There Yet?" at the September 2016 Embedded Vision Alliance Member Meeting. Gagvani provides an insider's perspective on vision-enabled video surveillance applications.

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Vision Processing Opportunities in Drones

UAVs (unmanned aerial vehicles), commonly known as drones, are a rapidly growing market and increasingly leverage embedded vision technology for digital video stabilization, autonomous navigation, and terrain analysis, among other functions. This article reviews drone market sizes and trends, and then discusses embedded vision technology applications in drones, such as image quality optimization, autonomous navigation,

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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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“Techniques for Efficient Implementation of Deep Neural Networks,” a Presentation from Stanford

Song Han, graduate student at Stanford, delivers the presentation "Techniques for Efficient Implementation of Deep Neural Networks" at the March 2016 Embedded Vision Alliance Member Meeting. Song presents recent findings on techniques for the efficient implementation of deep neural networks.

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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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Vision in Wearable Devices: Enhanced and Expanded Application and Function Choices

A version of this article was originally published at EE Times' Embedded.com Design Line. It is reprinted here with the permission of EE Times. 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

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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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