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
Professor Brian Lovell of the University of Queensland, Australia, who's also Chief Technical Officer at Imagus Technology, is a well-known figure in the fields of fields of computer vision and pattern recognition. Lovell is also a long-time advisor to (and advocate of) the Embedded Vision Alliance. On Tuesday November 5 at 9AM PT, Lovell and
Face Recognition: Learn About GPU Acceleration Read More +
by Tom Wilson CogniVue Brian Dipert Embedded Vision Alliance This article was originally published at Electronic Engineering Journal. It is reprinted here with the permission of TechFocus Media. Courtesy of service provider subsidies coupled with high shipment volumes, relatively inexpensive smartphones and tablets supply formidable processing capabilities: multi-core GHz-plus CPUs and graphics processors, on-chip DSPs
Embedded Vision on Mobile Devices: Opportunities and Challenges Read More +
For those of you who haven't already heard, Microsoft unveiled its next-generation Xbox One game console in late May, containing a bundled next-generation "Kinect 2.0" peripheral. Whereas the first-generation Kinect employs a structured light approach to 3-D sensing, "Kinect 2.0" leverages a time-of-flight technique courtesy of Microsoft's 2010 acquisition of Canesta. The included image sensor
Microsoft Kinect For Windows 2.0: Developer Registration Is A “Go” Read More +
Professor Kristen Grauman of the University of Texas at Austin presents the keynote on machine learning at the December 2012 Embedded Vision Alliance Member Summit. Grauman is a rising star in computer vision research. Among other distinctions, she was recently recognized with a Regents' Outstanding Teaching Award and, along with Devi Parikh, received the prestigious
“Machine Learning,” a Presentation from UT Austin Read More +
Embedded Vision Alliance Editor-in-Chief (and BDTI Senior Analyst) Brian Dipert and BDTI Senior Software Engineer Eric Gregori co-deliver an embedded vision application technology trends presentation at the December 2012 Embedded Vision Alliance Member Summit. Brian and Eric discuss embedded vision opportunities in mobile electronics devices. They quantify the market sizes and trends for smartphones and
December 2012 Embedded Vision Alliance Member Summit Technology Trends Presentation Read More +
Google didn't evolve the project name when incrementing from Android 4.1 (introduced in late June) to the more recent and latest 4.2 release. However, the newest "Jelly Bean" version makes several notable imaging improvements that will be of interest to embedded vision application developers. First off is Photo Sphere, an enhanced version of the traditional
Android 4.2: Still “Jelly Bean”, But A Beefier Panorama Mode And Other Features For You 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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