Object Identification

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

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

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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“Words, Pictures, and Common Sense: Visual Question Answering,” a Presentation from Facebook and Georgia Tech

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

Devi Parikh, Research Scientist at Facebook AI Research (FAIR) and Assistant Professor at Georgia Tech, presents the “Words, Pictures, and Common Sense: Visual Question Answering” tutorial at the May 2018 Embedded Vision Summit. Wouldn’t it be nice if machines could understand content in images and communicate this understanding as effectively as humans? Such technology would

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“Generative Sensing: Reliable Recognition from Unreliable Sensor Data,” a Presentation from Arizona State University

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

Lina Karam, Professor and Computer Engineering Director at Arizona State University, presents the “Generative Sensing: Reliable Recognition from Unreliable Sensor Data” tutorial at the May 2018 Embedded Vision Summit. While deep neural networks (DNNs) perform on par with – or better than – humans on pristine high-resolution images, DNN performance is significantly worse than human

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May 2018 Embedded Vision Summit Slides

Aerospace and Defense, Algorithms, Augmented Reality, Automotive, Biometrics, Downloads, Edge AI and Vision Alliance, Emotion Discernment, Entertainment, Face Detection, Face Recognition, Gesture Control, Industrial Vision (Computer Vision), Information Access and Analytics, Medical, Object Identification, Object Tracking, Optical Character Recognition, Processors, Retail, Robotics, Security, Sensors and Cameras, Software, Summit 2018, Tools /

The Embedded Vision Summit was held on May 21-24, 2018 in Santa Clara, California, as an educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations delivered at the Summit are listed below. All of the slides from these presentations are included in… May 2018 Embedded Vision Summit

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“A New Approach to Mass Transit Security,” a Presentation from Lux Research

Edge AI and Vision Alliance, Face Recognition, Object Identification, Object Tracking, Processors, Security, Sensors and Cameras, Software, Videos /

Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "A New Approach to Mass Transit Security" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Bünger presents a revolutionary computer-vision-based methodology for public transit safety.

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“Instrumenting Greenhouses as Data-driven Manufacturing Facilities,” a Presentation from IUNU

Algorithms, Edge AI and Vision Alliance, Object Identification, Processors, Sensors and Cameras, Software, Tools, Videos /

Matt King, Chief Technology Officer at IUNU, delivers the presentation "Instrumenting Greenhouses as Data-driven Manufacturing Facilities" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. King explains how his company is enabling increased efficiency in commercial greenhouses using robotic cameras, computer vision and machine learning.

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Data Sets for Machine Learning Model Training

AImotive, Algorithms, Articles, BDTI, Edge AI and Vision Alliance, iMerit, LUXOFT, Object Identification, Processors, Software, Technical Articles, Tools /

Deep learning and other machine learning techniques have rapidly become a transformative force in computer vision. Compared to conventional computer vision techniques, machine learning algorithms deliver superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence,

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“Using Computer Vision and Machine Learning to Understand Pet Behavior,” a Presentation from PetCube

Algorithms, Edge AI and Vision Alliance, Entertainment, Object Identification, Object Tracking, Security, Sensors and Cameras, Videos /

Alex Neskin, founder and CTO of PetCube, delivers the presentation "Using Computer Vision and Machine Learning to Understand Pet Behavior" at the Embedded Vision Alliance's December 2017 Vision Industry and Technology Forum. Neskin explains how his start-up is using vision and AI to improve the lives of pets and their owners.

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Figure2

Stereo Vision: Facing the Challenges and Seeing the Opportunities for ADAS Applications

Algorithms, Articles, Automotive, Object Identification, Object Tracking, Processors, Sensors and Cameras, Software, Technical Articles, Texas Instruments, Tools /

This technical article was originally published on Texas Instruments' website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction Cameras are the most precise mechanisms used to capture accurate data at high resolution. Like human eyes, cameras capture the resolution, minutiae and vividness of a scene with such beautiful detail that no

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“End to End Fire Detection Deep Neural Network Platform,” a Presentation from Tyco Innovation

Algorithms, Edge AI and Vision Alliance, Object Identification, Processors, Security, Sensors and Cameras, Software, Tools, Videos /

Divya Jain, Technical Director at Tyco Innovation, presents the "End to End Fire Detection Deep Neural Network Platform" tutorial at the May 2017 Embedded Vision Summit. This presentation dives deep into a real-world problem of fire detection to see what it takes to build a complete solution using CNNs. Fire is specifically challenging because it

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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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