Algorithms

“End to End Fire Detection Deep Neural Network Platform,” a Presentation from Tyco Innovation

Divya Jain, Technical Director at Tyco Innovation, presents the "End to End Fire Detection Deep Neural Network Platform" tutorial at the May 2017 Embedded Vision Summit. This presentation dives deep into a real-world problem of fire detection to see what it takes to build a complete solution using CNNs. Fire is specifically challenging because it […]

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“Designing CNN Algorithms for Real-time Applications,” a Presentation from Almond AI

Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit. The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used

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“The Coming Shift from Image Sensors to Image Sensing,” a Presentation from LG

Paul Gallagher, Senior Director of Technology and Product Planning for LG, presents the "Coming Shift from Image Sensors to Image Sensing" tutorial at the May 2017 Embedded Vision Summit. The image sensor space is entering the fourth disruption in its evolution. The first three disruptions primarily focused on taking “pretty pictures” for human consumption, evaluation,

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“Intelligent Video Surveillance: Are We There Yet?,” a Presentation from CheckVideo

Nik Gagvani, Founder and President of CheckVideo, presents the "Intelligent Video Surveillance: Are We There Yet?" tutorial at the May 2017 Embedded Vision Summit. The video surveillance market has been an early adopter of computer vision technology. After more than a decade of experience with deployed systems, what have we learned? This talk covers the

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“Intelligent Video Surveillance: Are We There Yet?,” a Presentation from CheckVideo

Nik Gagvani, Founder and President of CheckVideo, presents the "Intelligent Video Surveillance: Are We There Yet?" tutorial at the May 2017 Embedded Vision Summit. The video surveillance market has been an early adopter of computer vision technology. After more than a decade of experience with deployed systems, what have we learned? This talk covers the

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“Edge Intelligence: Visual Reinforcement Learning for Mobile Devices,” a Presentation from Imagry

Adham Ghazali, co-founder and CEO of Imagry, presents the "Edge Intelligence: Visual Reinforcement Learning for Mobile Devices" tutorial at the May 2017 Embedded Vision Summit. Real-life visual data encompasses a tremendous amount of information and presents a huge challenge for the design and development of a perceptual engine. Smart machines equipped with visual understanding technology

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“Another Set of Eyes: Machine Vision Automation Solutions for In Vitro Diagnostics,” a Presentation from Microscan Systems

Sadie Zeller, Manager of Global Product Management and the Clinical Vertical Market at Microscan Systems, presents the "Another Set of Eyes: Machine Vision Automation Solutions for In Vitro Diagnostics" tutorial at the May 2017 Embedded Vision Summit. In vitro diagnostics (IVD) are tests that can detect diseases, conditions, or infections. The use of automation, including

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“Using Markerless Motion Capture to Win Baseball Games,” a Presentation from KinaTrax

Steven Cadavid, President of KinaTrax, presents the "Using Markerless Motion Capture to Win Baseball Games" tutorial at the May 2017 Embedded Vision Summit. KinaTrax develops a markerless motion capture system that computes the kinematic data of an in-game baseball pitch. The system is installed in several Major League Baseball ballparks including Wrigley Field, home of

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“A Fast Object Detector for ADAS using Deep Learning,” a Presentation from Panasonic

Minyoung Kim, Senior Research Engineer at Panasonic Silicon Valley Laboratory, presents the "A Fast Object Detector for ADAS using Deep Learning" tutorial at the May 2017 Embedded Vision Summit. Object detection has been one of the most important research areas in computer vision for decades. Recently, deep neural networks (DNNs) have led to significant improvement

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“Unsupervised Everything,” a Presentation from Panasonic

Luca Rigazio, Director of Engineering for the Panasonic Silicon Valley Laboratory, presents the "Unsupervised Everything" tutorial at the May 2017 Embedded Vision Summit. The large amount of multi-sensory data available for autonomous intelligent systems is just astounding. The power of deep architectures to model these practically unlimited datasets is limited by only two factors: computational

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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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