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Vision Processing Opportunities in Drones

UAVs (unmanned aerial vehicles), commonly known as drones, are a rapidly growing market and increasingly leverage embedded vision technology for digital video stabilization, autonomous navigation, and terrain analysis, among other functions. This article reviews drone market sizes and trends, and then discusses embedded vision technology applications in drones, such as image quality optimization, autonomous navigation, …

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“Dataflow: Where Power Budgets Are Won and Lost,” a Presentation from Movidius

Sofiane Yous, Principal Scientist in the machine intelligence group at Movidius, presents the "Dataflow: Where Power Budgets Are Won and Lost" tutorial at the May 2016 Embedded Vision Summit. This presentation showcases stories from the front lines in the battle between power and performance in embedded vision, deep learning and computational imaging applications. First, Youse …

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“Using the OpenCL C Kernel Language for Embedded Vision Processors,” a Presentation from Synopsys

Seema Mirchandaney, Engineering Manager for Software Tools at Synopsys, presents the "Using the OpenCL C Kernel Language for Embedded Vision Processors" tutorial at the May 2016 Embedded Vision Summit. OpenCL C is a programming language that is used to write computation kernels. It is based on C99 and extended to support features such as multiple …

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“NVIDIA VisionWorks, a Toolkit for Computer Vision,” a Presentation from NVIDIA

Elif Albuz, Technical Lead for the VisionWorks Toolkit at NVIDIA, presents the "NVIDIA VisionWorks, a Toolkit for Computer Vision" tutorial at the May 2016 Embedded Vision Summit. In this talk, Albuz introduces the NVIDIA VisionWorks toolkit, a software development package for computer vision and image processing. VisionWorks implements and extends the Khronos OpenVX standard, and …

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“High-resolution 3D Reconstruction on a Mobile Processor,” a Presentation from Qualcomm

Michael Mangan, Product Manager for Camera and Computer Vision at Qualcomm, presents the "High-resolution 3D Reconstruction on a Mobile Processor" tutorial at the May 2016 Embedded Vision Summit. Computer vision has come a long way. Use cases that were previously not possible in mass-market devices are now more accessible thanks to advances in depth sensors …

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“Getting from Idea to Product with 3D Vision,” a Presentation from Intel and MathWorks

Anavai Ramesh, Senior Software Engineer at Intel, and Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, present the "Getting from Idea to Product with 3D Vision" tutorial at the May 2016 Embedded Vision Summit. To safely navigate autonomously, cars, drones and robots need to understand their surroundings in three dimensions. While 3D vision …

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“Should Visual Intelligence Reside in the Cloud or at the Edge? Trade-offs in Privacy, Security and Performance,” a Presentation from Silk Labs

Andreas Gal, CEO of Silk Labs, presents the "Should Visual Intelligence Reside in the Cloud or at the Edge? Trade-offs in Privacy, Security and Performance" tutorial at the May 2016 Embedded Vision Summit. The Internet of Things continues to expand and develop, including more intelligent connected devices that respond to people’s needs and alert them …

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“Using SGEMM and FFTs to Accelerate Deep Learning,” a Presentation from ARM

Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit. Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, …

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Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations

Neural networks enable the identification of objects in still and video images with impressive speed and accuracy after an initial training phase. This so-called "deep learning" has been enabled by the combination of the evolution of traditional neural network techniques, with one latest-incarnation example known as a CNN (convolutional neural network), by the steadily increasing …

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“Video Stabilization Using Computer Vision: Techniques for Embedded Devices,” a Presentation from CEVA

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 …

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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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