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Vision Processing Opportunities in Virtual Reality

Algorithms, Articles, Edge AI and Vision Alliance, Entertainment, Infineon, Intel, onsemi, Processors, Sensors, Software, Technical Articles, videantis /

VR (virtual reality) systems are beginning to incorporate practical computer vision techniques, dramatically improving the user experience as well as reducing system cost. This article provides an overview of embedded vision opportunities in virtual reality systems, such as environmental mapping, gesture interface, and eye tracking, along with implementation details. It also introduces an industry alliance […]

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Vision Processing Opportunities in Drones

Algorithms, Articles, Edge AI and Vision Alliance, Face Detection, Face Recognition, Intel, NVIDIA, NXP Semiconductors, Object Identification, Object Tracking, Processors, Robotics, Sensors, Software, Technical Articles, Tools, videantis, Xperi /

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

Algorithms, Intel, Processors, Software, Tools, Videos /

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

Algorithms, Intel, MathWorks, Processors, Sensors, Software, Tools, Videos /

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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FPGAs for Deep Learning-based Vision Processing

Articles, Intel, Technical Articles /

FPGAs have proven to be a compelling solution for solving deep learning problems, particularly when applied to image recognition. The advantage of using FPGAs for deep learning is primarily derived from several factors: their massively parallel architectures, efficient DSP resources, and large amounts of on-chip memory and bandwidth. An illustration of a typical FPGA architecture

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Figure3

Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations

Algorithms, Articles, BDTI, Cadence, Edge AI and Vision Alliance, Intel, Processors, Software, Synopsys, Technical Articles, Tools /

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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“Accelerating Deep Learning Using Altera FPGAs,” a Presentation from Intel

Algorithms, Intel, Processors, Software, Tools, Videos /

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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“Real-world Vision Systems Design: Challenges and Techniques,” a Presentation from Intel

Algorithms, Edge AI and Vision Alliance, Intel, Software, Tools, Videos /

Yury Gorbachev, Principal Engineer at Itseez (now part of Intel), presents the "Real-world Vision Systems Design: Challenges and Techniques" tutorial at the May 2016 Embedded Vision Summit. Computer vision is central to many modern, cool products and technologies, including augmented reality, virtual reality and drones. Thanks to recent advances in system-on-chip and embedded systems design,

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Movidius Brings Artificial Vision Intelligence to FLIR Systems’ Latest Thermal Imaging Product

FLIR Systems, Intel, News /

FLIR's New Boson™ Thermal Camera Core to Feature Onboard Visual Intelligence Computing Through Custom Implementation of Myriad 2 VPU SAN MATEO, CA–(Marketwired – Apr 18, 2016) – Movidius, the leader in low-power machine vision, today announced a strategic collaboration with FLIR Systems, a global leader in thermal imaging technology, to bring advanced computer vision capabilities

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Efficient Implementation of Neural Network Systems Built on FPGAs, Programmed with OpenCL

Articles, Intel, Technical Articles /

This technical article was originally published at Altera's website. It is reprinted here with the permission of Altera. Deep learning neural network systems currently provide the best solution to many large computing problems for image recognition and natural language processing. Neural networks are inspired by biological systems, in particular the human brain; they use conventional

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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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