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 […]
May 2016 Embedded Vision Summit Proceedings Read More +
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.
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
Deep Learning Use Cases for Computer Vision (Download) Read More +
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
Using Convolutional Neural Networks for Image Recognition Read More +
Alon Atsmon, Vice President of Technology Strategy at Harman International, presents the "Harman’s Augmented Navigation Platform—The Convergence of ADAS and Navigation" tutorial at the May 2015 Embedded Vision Summit. Until recently, advanced driver assistance systems (ADAS) and in-car navigation systems have evolved as separate standalone systems. Today, however, the combination of available embedded computing power
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
Vision in Wearable Devices: Enhanced and Expanded Application and Function Choices Read More +
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
Accelerate Machine Learning with the cuDNN Deep Neural Network Library Read More +
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
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
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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