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Computer Vision for Augmented Reality in Embedded Designs

Algorithms, Articles, Augmented Reality, Edge AI and Vision Alliance, NXP Semiconductors, Processors, Sensors and Cameras, Software, Synopsys, Technical Articles, Tools, videantis /

Augmented reality (AR) and related technologies and products are becoming increasingly popular and prevalent, led by their adoption in smartphones, tablets and other mobile computing and communications devices. While developers of more deeply embedded platforms are also motivated to incorporate AR capabilities in their products, the comparative scarcity of processing, memory, storage, and networking resources […]

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“The Roomba 980: Computer Vision Meets Consumer Robotics,” a Presentation from iRobot

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Mario Munich, Senior Vice President of Technology at iRobot, presents the “Roomba 980: Computer Vision Meets Consumer Robotics” tutorial at the May 2018 Embedded Vision Summit. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. The availability of affordable electro-mechanical components, powerful embedded microprocessors and

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“How Simulation Accelerates Development of Self-Driving Technology,” a Presentation from AImotive

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László Kishonti, founder and CEO of AImotive, presents the “How Simulation Accelerates Development of Self-Driving Technology” tutorial at the May 2018 Embedded Vision Summit. Virtual testing, as discussed by Kishonti in this presentation, is the only solution that scales to address the billions of miles of testing required to make autonomous vehicles robust. However, integrating

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“Recognizing Novel Objects in Novel Surroundings with Single-shot Detectors,” a Presentation from the University of North Carolina at Chapel Hill

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Alexander C Berg, Associate Professor at the University of North Carolina at Chapel Hill and CTO of Shopagon, presents the “Recognizing Novel Objects in Novel Surroundings with Single-shot Detectors” tutorial at the May 2018 Embedded Vision Summit. Berg’s group’s 2016 work on single-shot object detection (SSD) reduced the computation cost for accurate detection of object

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“Building a Typical Visual SLAM Pipeline,” a Presentation from Virgin Hyperloop One

Algorithms, Edge AI and Vision Alliance, Object Tracking, Processors, Software, Summit 2018, Tools, Videos /

YoungWoo Seo, Senior Director at Virgin Hyperloop One, presents the “Building a Typical Visual SLAM Pipeline” tutorial at the May 2018 Embedded Vision Summit. Maps are important for both human and robot navigation. SLAM (simultaneous localization and mapping) is one of the core techniques for map-based navigation. As SLAM algorithms have matured and hardware has

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“Developing Computer Vision Algorithms for Networked Cameras,” a Presentation from Intel

Algorithms, Intel, Processors, Security, Sensors and Cameras, Software, Summit 2018, Tools, Videos /

Dukhwan Kim, computer vision software architect at Intel, presents the “Developing Computer Vision Algorithms for Networked Cameras” tutorial at the May 2018 Embedded Vision Summit. Video analytics is one of the key elements in network cameras. Computer vision capabilities such as pedestrian detection, face detection and recognition and object detection and tracking are necessary for

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“Visual-Inertial Tracking for AR and VR,” a Presentation from Meta

Algorithms, Augmented Reality, Edge AI and Vision Alliance, Processors, Sensors and Cameras, Software, Summit 2018, Tools, Videos /

Timo Ahonen, Director of Engineering for Computer Vision at Meta, presents the “Visual-Inertial Tracking for AR and VR” tutorial at the May 2018 Embedded Vision Summit. This tutorial covers the main current approaches to solving the problem of tracking the motion of a display for AR and VR use cases. Ahonen covers methods for inside-out

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“Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application,” a Presentation from AMD

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Mike Schmit, Director of Software Engineering for computer vision and machine learning at AMD, presents the “Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application” tutorial at the May 2018 Embedded Vision Summit. When training deep neural networks, having the right training data is key. In this talk, Schmit explores

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“Understanding and Implementing Face Landmark Detection and Tracking,” a Presentation from PathPartner Technology

Algorithms, Face Detection, Face Recognition, PathPartner Technology, Processors, Sensors and Cameras, Software, Summit 2018, Tools, Videos /

Jayachandra Dakala, Technical Architect at PathPartner Technology, presents the “Understanding and Implementing Face Landmark Detection and Tracking” tutorial at the May 2018 Embedded Vision Summit. Face landmark detection is of profound interest in computer vision, because it enables tasks ranging from facial expression recognition to understanding human behavior. Face landmark detection and tracking can be

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“From Feature Engineering to Network Engineering,” a Presentation from ShatterLine Labs and AMD

Algorithms, AMD, Processors, Software, Summit 2018, Tools, Videos /

Auro Tripathy, Founding Principal at ShatterLine Labs (representing AMD), presents the “From Feature Engineering to Network Engineering” tutorial at the May 2018 Embedded Vision Summit. The availability of large labeled image datasets is tilting the balance in favor of “network engineering”instead of “feature engineering”. Hand-designed features dominated recognition tasks in the past, but now features

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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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