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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Ori Inbar, co-founder and CEO of AugmentedReality.org, presents the "How to Make the World More Interactive: Augmented Reality as the Interface Between Wearable Tech and the Internet of Things" tutorial at the May 2014 Embedded Vision Summit. In this talk, Inbar explains how augmented reality, which relies heavily on embedded vision, is transitioning from a
Goksel Dedeoglu, Ph.D., Founder and Lab Director of PercepTonic, presents the "Embedded Lucas-Kanade Tracking: How It Works, How to Implement It, and How to Use It" tutorial at the May 2014 Embedded Vision Summit. This tutorial is intended for technical audiences interested in learning about the Lucas-Kanade (LK) tracker, also known as the Kanade-Lucas-Tomasi (KLT)
Simon Morris, CEO of CogniVue, presents the "Evolving Algorithmic Requirements for Recognition and Classification in Augmented Reality" tutorial at the May 2014 Embedded Vision Summit. Augmented reality (AR) applications are based on accurately computing a camera's 6 degrees of freedom (6DOF) position in 3-dimensional space, also known as its "pose". In vision-based approaches to AR,
Francis MacDougall, Senior Director of Technology at Qualcomm, presents the "Vision-Based Gesture User Interfaces" tutorial at the May 2014 Embedded Vision Summit. The means by which we interact with the machines around us is undergoing a fundamental transformation. While we may still sometimes need to push buttons, touch displays and trackpads, and raise our voices,
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
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
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
This article was originally published at Electronic Engineering Journal. It is reprinted here with the permission of TechFocus Media. Although augmented reality was first proposed and crudely demonstrated nearly fifty years ago, its implementation was until recently only possible on bulky and expensive computers. Nowadays, however, the fast, low power and cost-effective processors and high
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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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