Object Identification Functions

“Object Detection for Embedded Markets,” a Presentation from Imagination Technologies

Paul Brasnett, PowerVR Business Development Director for Vision and AI at Imagination Technologies, presents the “Object Detection for Embedded Markets” tutorial at the May 2019 Embedded Vision Summit. While image classification was the breakthrough use case for deep learning-based computer vision, today it has a limited number of real-world applications. In contrast, object detection is

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“Teaching Machines to See, Understand, Describe and Predict Sports Games in Real Time,” a Presentation from Sportlogiq

Mehrsan Javan, CTO of Sportlogiq, presents the "Teaching Machines to See, Understand, Describe and Predict Sports Games in Real Time" tutorial at the May 2019 Embedded Vision Summit. Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this means designing a fully-automated, robust, end-to-end pipeline; from visual input,

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“Addressing Corner Cases in Embedded Computer Vision Applications,” a Presentation from Netradyne

David Julian, CTO and Founder of Netradyne, presents the "Addressing Corner Cases in Embedded Computer Vision Applications" tutorial at the May 2019 Embedded Vision Summit. Many embedded vision applications require solutions that are robust in the face of very diverse real-world inputs. For example, in automotive applications, vision-based safety systems may encounter unusual configurations of

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“Visual AI Enables Autonomous Security,” an Interview with Knightscope

William "Bill" Santana Li, Co-founder, Chairman and CEO of Knightscope, talks with Vin Ratford, Executive Director of the Embedded Vision Alliance, for the "Visual AI Enables Autonomous Security" interview at the May 2019 Embedded Vision Summit. Knightscope, a physical security technologies company based in Silicon Valley, develops and sells a line of autonomous robots that

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May 2019 Embedded Vision Summit Slides

The Embedded Vision Summit was held on May 20-23, 2019 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 PDF form. To download the

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“How to Get the Labeled Data for Free,” a Presentation from IUNU

Matt King, CTO of IUNU, presents the "How to Get the Labeled Data for Free" tutorial at the May 2018 Embedded Vision Summit. Contextual machine understanding depends on labeled data, and gathering this data can be a significant constraint on growth. Paying for data, specifically paying for labeled data, is putting the cart before the

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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

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

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

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

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 PDF form. To download the

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Data Sets for Machine Learning Model Training

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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