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The listing below showcases the most recently published content associated with various AI and visual intelligence functions.

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Integrate Face Detection in Your Application

This blog post was originally published at Xailient’s website. It is reprinted here with the permission of Xailient. Don’t be overwhelmed, because adding face detection to your applications has never been so easy! A lot of the times, face detection is not the primary focus of the application but is an important component and many

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“Case Study: Facial Detection and Recognition for Always-On Applications,” a Presentation from Synopsys

Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit. Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on,… “Case Study: Facial Detection and

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“A Mask Detection Smart Camera Using the Nvidia Jetson Nano: System Architecture and Developer Experience,” a Presentation from BDTI and Tryolabs

Evan Juras, Computer Vision Engineer at BDTI, and Braulio Ríos, Machine Learning Engineer at Tryolabs, co-present the “A Mask Detection Smart Camera Using the Nvidia Jetson Nano: System Architecture and Developer Experience” tutorial at the May 2021 Embedded Vision Summit. MaskCam is a prototype reference design for a smart camera… “A Mask Detection Smart Camera

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“Object Detection and Dataset Labeling Using Colors of Manufactured Objects,” a Presentation from BASF

Ian Childers, Head of Technology for Functional Coatings—Object Recognition at BASF, presents the “Object Detection and Dataset Labeling Using Colors of Manufactured Objects” tutorial at the May 2021 Embedded Vision Summit. This talk introduces a new method for object detection for consumer goods and other applications based on measuring an… “Object Detection and Dataset Labeling

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NVIDIA Research: Fast Uncertainty Quantification for Deep Object Pose Estimation

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Researchers from NVIDIA, University of Texas at Austin and Caltech developed a simple, efficient, and plug-and-play uncertainty quantification method for the 6-DoF (degrees of freedom) object pose estimation task, using an ensemble of K pre-trained estimators with

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Struggles of Running Object Detection on a Raspberry Pi

Frustrated man (Image by Gerd Altmann from Pixabay) This blog post was originally published at Xailient’s website. It is reprinted here with the permission of Xailient. Have you ever been so excited after reading a how-to tutorial or a github readme file, only to discover after following it through that it is not really as

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Getting Machines to Understand Human Emotions

This blog post was originally published at Intel’s website. It is reprinted here with the permission of Intel. Throughout season two of the Intel on AI podcast, many of the world’s most prominent artificial intelligence (AI) experts have talked about why they believe machines can’t simply improve performance on large data sets in order for

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September 2020 Embedded Vision Summit Slides

The Embedded Vision Summit was held online on September 15-25, 2020, 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.… September 2020 Embedded Vision Summit

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“A Computer Vision-Based Personal Trainer That Runs On Your Phone,” a Presentation from Twenty BN

Roland Memisevic, CEO, Chief Scientist and Founder of Twenty BN, presents the “Computer Vision-Based Personal Trainer That Runs On Your Phone” tutorial at the September 2020 Embedded Vision Summit. In this presentation, Memisevic describes Twenty BN’s journey towards building an AI-powered personal trainer that runs on your phone. The trainer takes the form of an

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