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
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
May 2014 Embedded Vision Summit Proceedings Read More +
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
Embedded Vision: Enabling Smarter Mobile Apps and Devices Read More +
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
Augmented Reality: A Compelling Mobile Embedded Vision Opportunity Read More +
Neil Trevett, President of Khronos and Vice President at NVIDIA, delivers the standards presentation, "Recent Developments in Khronos Standards for Embedded Vision," at the March 2014 Embedded Vision Alliance Member Meeting.
Do you want to write your own blazing fast, interactive mobile apps using computer vision technology? Apps that can make your camera smarter, find people's faces, understand their gestures, interpret scenes and augment them with graphics? The Tegra super chip and the OpenCV for Tegra library can help you to do just that! OpenCV for
Introduction to OpenCV for Tegra Read More +
There is a growing need for fast and power-efficient computer vision on embedded devices. This session will focus on computer vision capabilities on embedded platforms available to ADAS developers, covering the OpenCV CUDA implementation and the new computer vision standard, OpenVX. In addition, Itseez traffic sign detection will be showcased. The algorithm is capable of
Real-Time Traffic Sign Recognition on Mobile Processors Read More +
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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