Object Identification

“Bringing Computer Vision to the Consumer,” a Keynote Presentation from Dyson

Mike Aldred, Electronics Lead at Dyson, presents the "Bringing Computer Vision to the Consumer" keynote at the May 2015 Embedded Vision Summit. While vision has been a research priority for decades, the results have often remained out of reach of the consumer. Huge strides have been made, but the final, and perhaps toughest, hurdle is […]

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“The Evolution of Object Recognition in Embedded Systems,” a Presentation from CEVA

Moshe Shahar, Director of System Architecture at CEVA, presents the "Evolution of Object Recognition in Embedded Systems" tutorial at the May 2015 Embedded Vision Summit. Camera-enabled devices have made great strides in performance and quality in recent years, but they still fall far short of human visual perception. To reach their potential, vision-enabled systems must

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“Keeping Brick and Mortar Relevant, A Look Inside Retail Analytics,” A Presentation from Prism Skylabs

Doug Johnston, Founder and Vice President of Technology at Prism Skylabs, delivers the presentation "Keeping Brick and Mortar Relevant: A Look Inside Prism Skylabs and Retail Analytics" at the December 2014 Embedded Vision Alliance Member Meeting. Doug explains how his firm is using vision to provide retailers with actionable intelligence based on consumer behavior.

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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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nvidia

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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“Convolutional Neural Networks,” an Embedded Vision Summit Keynote Presentation from Facebook

Yann LeCun, Director of AI Research at Facebook and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University, presents the "Convolutional Networks: Unleashing the Potential of Machine Learning for Robust Perception Systems" keynote at the May 2014 Embedded Vision Summit. Convolutional Networks (ConvNets) have become the dominant method

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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

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May 2014 Embedded Vision Summit Technical Presentation: “How to Create a Great Object Detector,” Avinash Nehemiah, MathWorks

Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Create a Great Object Detector" tutorial at the May 2014 Embedded Vision Summit. Detecting objects of interest in images and video is a key part of practical embedded vision systems. Impressive progress has been made over the past few years by

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johnday-blog

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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“Computational Photography: An Introduction and Highlights of Recent Research,” a Presentation from the University of Wisconsin

Professor Li Zhang of the University of Wisconsin presents an introduction to computational photography at the December 2013 Embedded Vision Alliance Member Meeting.

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