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

Algorithms, Processors, Qualcomm, Sensors and Cameras, Software, Tools, Videos /

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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Embedded Vision Application: A Design Approach for Real Time Classifiers

Articles, PathPartner Technology, Technical Articles /

This article was originally published at PathPartner Technology's website. It is reprinted here with the permission of PathPartner Technology. Object detection/classification is a supervised learning process in machine vision to recognize patterns or objects from images or other data. It is a major component in Advanced Driver Assistance Systems (ADAS), for example, as it is

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

Algorithms, Intel, MathWorks, Processors, Sensors and Cameras, 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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Embedded Vision Insights: August 2, 2016 Edition

Edge AI and Vision Alliance, Newsletters /

FEATURED VIDEOS Combining Flexibility and Low-Power in Embedded Vision Subsystems: An Application to Pedestrian Detection Bruno Lavigueur, Embedded Vision Subsystem Project Leader at Synopsys, presents a case study of a pedestrian detection application. Starting from a high-level functional description in OpenCV, he decomposes and maps the application onto a heterogeneous platform consisting of a high-performance

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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

Algorithms, Edge AI and Vision Alliance, Privacy, Processors, Software, Videos /

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

Algorithms, Arm, Processors, Software, Tools, Videos /

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

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

Algorithms, Ceva, Processors, Software, Tools, Videos /

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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“Digital Gimbal: Rock-steady Video Stabilization without Extra Weight!,” a Presentation from FotoNation

Algorithms, Sensors and Cameras, Software, Videos, Xperi /

Petronel Bigioi, Senior Vice President of Engineering and General Manager at FotoNation, presents the "Digital Gimbal: Rock-steady Video Stabilization without Extra Weight!" tutorial at the May 2016 Embedded Vision Summit. In this presentation, you will learn about new hardware solutions that can process video at up to 60 fps, delivering rock-steady video that is practically

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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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