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“Programming Techniques for Implementing Inference Software Efficiently,” a Presentation from Codeplay Software

Andrew Richards, CEO and Founder of Codeplay Software, presents the “Programming Techniques for Implementing Inference Software Efficiently” tutorial at the May 2018 Embedded Vision Summit. When writing software to deploy deep neural network inferencing, developers are faced with an overwhelming range of options, from a custom-coded implementation of a single model to using a deep […]

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“Introduction to Creating a Vision Solution in the Cloud,” a Presentation from GumGum

Nishita Sant, Computer Vision Scientist at GumGum, presents the “Introduction to Creating a Vision Solution in the Cloud” tutorial at the May 2018 Embedded Vision Summit. A growing number of applications utilize cloud computing for execution of computer vision algorithms. In this presentation, Sant introduces the basics of creating a cloud-based vision service, based on

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“Building A Practical Face Recognition System Using Cloud APIs,” a Presentation from the Washington County Sheriff’s Office

Chris Adzima, Senior Information Systems Analyst for the Washington County Sheriff’s Office in Oregon, presents the “Building a Practical Face Recognition System Using Cloud APIs” tutorial at the May 2018 Embedded Vision Summit. In this presentation, Adzima walks through the design and implementation of a face recognition system utilizing cloud computing and cloud computer vision

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“Solving Vision Tasks Using Deep Learning: An Introduction,” a Presentation from Google

Pete Warden, Google research engineer and the tech lead of  the TensorFlow Mobile and Embedded team, presents the “Solving Vision Tasks Using Deep Learning: An Introduction” tutorial at the May 2018 Embedded Vision Summit. This talk introduces deep learning for vision tasks. It provides an overview of deep learning, explores its weaknesses and strengths, and

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“The Four Key Trends Driving the Proliferation of Visual Perception,” a Presentation from the Embedded Vision Alliance

Jeff Bier, Founder of the Embedded Vision Alliance and Co-founder and President of BDTI, presents the “Four Key Trends Driving the Proliferation of Visual Perception” tutorial at the May 2018 Embedded Vision Summit. With so much happening in computer vision applications and technology, and happening so fast, it can be difficult to see the big

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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… May 2018 Embedded Vision Summit

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

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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“The Caffe2 Framework for Mobile and Embedded Deep Learning,” a Presentation from Facebook

Fei Sun, software engineer at Facebook, delivers the presentation "The Caffe2 Framework for Mobile and Embedded Deep Learning" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Sun introduces Caffe2, a new open-source machine learning framework, and explains how Facebook is using it to enable computer vision in mobile and embedded devices.

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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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Implementing Vision with Deep Learning in Resource-constrained Designs

DNNs (deep neural networks) have transformed the field of computer vision, delivering 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, which had been solved quite well with conventional techniques, are now finding even better

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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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