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

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

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

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

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

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

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

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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“AI-powered Identity: Evaluating Face Recognition Capabilities,” a Presentation From the University of Houston

Ioannis Kakadiaris, Distinguished University Professor of Computer Science at the University of Houston, presents the "AI-powered Identity: Evaluating Face Recognition Capabilities" tutorial at the May 2019 Embedded Vision Summit. Following the deep learning renaissance, the face recognition community has achieved remarkable results when comparing images that are both frontal and non-occluded. However, significant challenges remain

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“Designing Home Monitoring Cameras for Scale,” a Presentation from Ring

Ilya Brailovskiy, Principal Engineer, and Changsoo Jeong, Head of Algorithm, both of Ring, present the "Optimizing SSD Object Detection for Low-power Devices" tutorial at the May 2019 Embedded Vision Summit. In this talk, Brailovskiy and Jeong discuss how Ring designs smart home video cameras to make neighborhoods safer. In particular, they focus on three key

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“Low-power Computer Vision: Status, Challenges and Opportunities,” a Presentation from Purdue University

Professor Yung-Hsiang Lu of Purdue University presents the "Low-power Computer Vision: Status, Challenges and Opportunities" tutorial at the May 2019 Embedded Vision Summit. Energy efficiency plays a crucial role in making computer vision successful in battery-powered systems, including drones, mobile phones and autonomous robots. Since 2015, IEEE has been organizing an annual competition on low-power

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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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