A Guide to Video Analytics: Applications and Opportunities

This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. Introduction In the past few years, video analytics, also known as video content analysis or intelligent video analytics, has attracted increasing interest from both industry and the academic world. Thanks to the enormous advances made in deep learning, …

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Machine Learning On Edge Devices: Benchmark Report

This article was originally published at Tryolabs' website. It is reprinted here with the permission of Tryolabs. Why edge computing? Humans are generating and collecting more data than ever. We have devices in our pockets that facilitate the creation of huge amounts of data, such as photos, gps coordinates, audio, and all kinds of personal …

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Cloud and Edge Vision Processing Options for Deep Learning Inference

Should deep learning-based computer vision processing take place in the cloud, at the edge, or both? This seemingly simple question has a complicated answer: "it depends." This article provides perspectives on the various factors you should consider, and with what priorities, when making this implementation decision for your particular project's requirements.

Rapid Prototyping on NVIDIA Jetson Platforms with MATLAB

This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. This article discusses how an application developer can prototype and deploy deep learning algorithms on hardware like the NVIDIA Jetson Nano Developer Kit with MATLAB. In previous posts, we explored how you can design and train deep learning …

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“Separable Convolutions for Efficient Implementation of CNNs and Other Vision Algorithms,” a Presentation from Phiar

Chen-Ping Yu, Co-founder and CEO of Phiar, presents the "Separable Convolutions for Efficient Implementation of CNNs and Other Vision Algorithms" tutorial at the May 2019 Embedded Vision Summit. Separable convolutions are an important technique for implementing efficient convolutional neural networks (CNNs), made popular by MobileNet’s use of depthwise separable convolutions. But separable convolutions are not …

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Automated Optical Inspection

This article was originally published at Basler's website. It is reprinted here with the permission of Basler. Optical Measurement Systems inspect objects and detect a variety of different characteristics. With its large selection of area scan and line scan cameras, Basler has the right model for any camera inspection task.

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