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, […]
Kees Vissers, Distinguished Engineer at Xilinx, presents the "Programming Novel Recognition Algorithms on Heterogeneous Architectures" tutorial at the May 2014 Embedded Vision Summit. The combination of heterogeneous systems, consisting of processors and FPGA, is a high-performance implementation platform for image and vision processing. One of the significant hurdles in leveraging the compute potential was the
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
Nathaniel Fairfield, Technical Lead at Google, presents the "Self-Driving Cars" keynote at the May 2014 Embedded Vision Summit. Self-driving cars have the potential to transform how we move: they promise to make us safer, give freedom to millions of people who can't drive, and give people back their time. The Google Self-Driving Car project was
“Self-Driving Cars,” an Embedded Vision Summit Keynote Presentation from Google Read More +
Chris Rowen, Fellow at Cadence, presents the "Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking" tutorial at the May 2014 Embedded Vision Summit. This presentation looks at a cross-section of advanced feature detectors, and considers the algorithm, bit precision, arithmetic primitives and implementation optimizations that yield high pixel processing rates,
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
The Embedded Vision Summit was held on May 29, 2014 in Santa Clara, California, as a technical educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The program for the event included the following presentations, whose PDF-formatted foilsets are available for download as a… May 2014 Embedded Vision Summit
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For decades, computer vision technology was found mainly in university laboratories and a few niche applications. Today, virtually every tablet and smartphone is capable of sophisticated vision functions such as hand gesture recognition, face recognition, gaze tracking, and object recognition. These capabilities are being used to enable new types of applications, user interfaces, and use
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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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