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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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Embedded Vision Insights: September 13, 2016 Edition

LETTER FROM THE EDITOR Dear Colleague, Next Thursday, September 22 from 9 am to 5 pm, the primary Caffe developers from U.C. Berkeley's Vision and Learning Center will present "Deep Learning for Vision Using CNNs and Caffe," a full-day detailed technical tutorial focused on convolutional neural networks (CNNs) for vision and the Caffe framework for

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Embedded Vision Insights: August 30, 2016 Edition

FEATURED VIDEOS "Tailoring Convolutional Neural Networks for Low-Cost, Low-Power Implementation," a Presentation from Synopsys Deep learning-based object detection using convolutional neural networks (CNN) has recently emerged as one of the leading approaches for achieving state-of-the-art detection accuracy for a wide range of object classes. Most of the current CNN-based detection algorithm implementations run on high-performance

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Embedded Vision Insights: August 16, 2016 Edition

FEATURED VIDEOS "3D from 2D: Theory, Implementation, and Applications of Structure from Motion," a Presentation from videantis Structure from motion uses a unique combination of algorithms that extract depth information using a single 2D moving camera. Using a calibrated camera, feature detection, and feature tracking, the algorithms calculate an accurate camera pose and a 3D

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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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May 2016 Embedded Vision Summit Introductory Presentation (Day 1)

Jeff Bier, Founder of the Embedded Vision Alliance, welcomes attendees to the May 2016 Embedded Vision Summit on May 2, 2016 (Day 1). Bier provides an overview of the embedded vision market opportunity, challenges, solutions and trends. He also introduces the Embedded Vision Alliance and the resources it offers for both product creators and potential

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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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Embedded Vision Application: A Design Approach for Real Time Classifiers

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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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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