Summit 2019

“Neuromorphic Event-based Vision: From Disruption to Adoption at Scale,” a Presentation from Prophesee

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Luca Verre, Co-founder and CEO of Prophesee, presents the “Neuromorphic Event-based Vision: From Disruption to Adoption at Scale” tutorial at the May 2019 Embedded Vision Summit. Neuromorphic event-based vision is a new paradigm in imaging technology, inspired by human biology. It promises to dramatically improve machines’ ability to sense their environments and make intelligent decisions […]

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“Deep Learning for Manufacturing Inspection Applications,” a Presentation from FLIR Systems

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Stephen Se, Research Manager at FLIR Systems, presents the “Deep Learning for Manufacturing Inspection Applications” tutorial at the May 2019 Embedded Vision Summit. Recently, deep learning has revolutionized artificial intelligence and has been shown to provide the best solutions to many problems in computer vision, image classification, speech recognition and natural language processing. Se presents

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

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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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“Deploying Visual SLAM in Low-power Devices,” a Presentation from CEVA

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Ben Weiss, Customer Solutions Engineer in the CSG Group at CEVA, presents the “Deploying Visual SLAM in Low-power Devices” tutorial at the May 2019 Embedded Vision Summit. Simultaneous localization and mapping (SLAM) technology has been evolving for quite some time, including visual SLAM, which relies primarily on image data. But implementing fast, accurate visual SLAM

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“Data Annotation at Scale: Pitfalls and Solutions,” a Presentation from Intel

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Nikita Manovich, Senior Software Engineer at Intel, presents the “Data Annotation at Scale: Pitfalls and Solutions” tutorial at the May 2019 Embedded Vision Summit. In many real-world use cases, deep learning algorithms work well if you have enough high-quality data to train them. Obtaining that data is a critical limiting factor in the development of

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“Machine Learning- based Image Compression: Ready for Prime Time?,” a Presentation from Clarifai

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Michael Gormish, Research Manager at Clarifai, presents the “Machine Learning- based Image Compression: Ready for Prime Time?” tutorial at the May 2019 Embedded Vision Summit. Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized

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“Portable Performance via the OpenVX Computer Vision Library: Case Studies,” a Presentation from Cadence

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Frank Brill, Design Engineering Director at Cadence, presents the “Portable Performance via the OpenVX Computer Vision Library: Case Studies” tutorial at the May 2019 Embedded Vision Summit. OpenVX is a state-of-the-art open API standard for accelerating applications using computer vision and machine learning. The API and its conformance tests enable applications to leverage highly specialized

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“Challenges and Approaches for Extracting Meaning from Satellite Imagery,” a Presentation from Orbital Insight

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Adam Kraft, Deep Learning Engineer at Orbital Insight, presents the "Challenges and Approaches for Extracting Meaning from Satellite Imagery" tutorial at the May 2019 Embedded Vision Summit. Orbital Insight is a geospatial big data company leveraging the rapidly growing availability of satellite, UAV and other geospatial data sources to understand and characterize socioeconomic trends at

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“Emerging Processor Architectures for Deep Learning: Options and Trade-offs,” a Presentation from Hailo

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Orr Danon, CEO of Hailo, presents the “Emerging Processor Architectures for Deep Learning: Options and Trade-offs” tutorial at the May 2019 Embedded Vision Summit. In the past year, numerous new processor architectures for machine learning have emerged. Many of these focus on edge applications, reflecting the growing demand for deploying machine learning outside of data

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“Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB,” a Presentation from MathWorks

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Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit. In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and

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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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