Ben Weiss, Computer Vision Developer at CEVA, presents the "Video Stabilization Using Computer Vision: Techniques for Embedded Devices" tutorial at the May 2016 Embedded Vision Summit. Today, video streams are increasingly captured by small, moving devices, including action cams, smartphones and drones. These devices enable users to capture video conveniently in a wide range of […]
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit. Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels
Allen Rush, Fellow at AMD, presents the "How Computer Vision Is Accelerating the Future of Virtual Reality" tutorial at the May 2016 Embedded Vision Summit. Virtual reality (VR) is the new focus for a wide variety of applications including entertainment, gaming, medical, science, and many others. The technology driving the VR user experience has advanced
Raj Talluri, Senior Vice President of Product Management at Qualcomm Technologies, presents the "Is Vision the New Wireless?" tutorial at the May 2016 Embedded Vision Summit. Over the past 20 years, digital wireless communications has become an essential technology for many industries, and a primary driver for the electronics industry. Today, computer vision is showing
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Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Efficient Convolutional Neural Network Inference on Mobile GPUs" tutorial at the May 2016 Embedded Vision Summit. GPUs have become established as a key tool for training of deep learning algorithms. Deploying those algorithms on end devices is a key enabler to their commercial success and
Bill Jenkins, Senior Product Specialist for High Level Design Tools at Intel, presents the "Accelerating Deep Learning Using Altera FPGAs" tutorial at the May 2016 Embedded Vision Summit. While large strides have recently been made in the development of high-performance systems for neural networks based on multi-core technology, significant challenges in power, cost and, performance
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Yair Siegel, Director of Segment Marketing at CEVA, presents the "Fast Deployment of Low-power Deep Learning on CEVA Vision Processors" tutorial at the May 2016 Embedded Vision Summit. Image recognition capabilities enabled by deep learning are benefitting more and more applications, including automotive safety, surveillance and drones. This is driving a shift towards running neural
Tim Hartley, Product Manager in the Personal Mobile Compute Business Line at ARM, presents the "Lessons Learned from Bringing Mobile and Embedded Vision Products to Market" tutorial at the May 2016 Embedded Vision Summit. Great news: technology is finally at a point where we can build sophisticated computer vision applications that run on mass market
Alexey Rybakov, Senior Director at Luxoft, presents the "Making Computer Vision Software Run Fast on Your Embedded Platform" tutorial at the May 2016 Embedded Vision Summit. Many computer vision algorithms perform well on desktop class systems, but struggle on resource constrained embedded platforms. This how-to talk provides a comprehensive overview of various optimization methods that
The substantial parallel processing resources available in modern graphics processors makes them a natural choice for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across GPUs and other heterogeneous processing elements within a system. In this article, we briefly review parallelism in computer vision applications,
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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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