Optical Character Recognition Functions
September 2020 Embedded Vision Summit Slides
The Embedded Vision Summit was held online on September 15-25, 2020, 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 PDF form.… September 2020 Embedded Vision Summit
“Visual AI Enables Autonomous Security,” an Interview with Knightscope
William “Bill” Santana Li, Co-founder, Chairman and CEO of Knightscope, talks with Vin Ratford, Executive Director of the Embedded Vision Alliance, for the “Visual AI Enables Autonomous Security” interview at the May 2019 Embedded Vision Summit. Knightscope, a physical security technologies company based in Silicon Valley, develops and sells a line of autonomous robots that
May 2019 Embedded Vision Summit Slides
The Embedded Vision Summit was held on May 20-23, 2019 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 2019 Embedded Vision Summit
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
“Vision Challenges in a Robotic Power Tool,” a Presentation from Shaper Tools
Alec Rivers, co-founder of Shaper Tools, presents the "Vision Challenges in a Robotic Power Tool" tutorial at the May 2017 Embedded Vision Summit. Shaper Tools has developed a first-of-its-kind robotic power tool enabled by embedded vision. Vision is used to track the tool's orientation in 3D at 100 Hz to an accuracy of 0.01 inches
“How to Test and Validate an Automated Driving System,” a Presentation from MathWorks
Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from
“PCI Express – A High-bandwidth Interface for Multi-camera Embedded Systems,” a Presentation from XIMEA
Max Larin, CEO of XIMEA, presents the "PCI Express – A High-bandwidth Interface for Multi-camera Embedded Systems" tutorial at the May 2017 Embedded Vision Summit. In this presentation, Larin provides an overview of existing camera interfaces for embedded systems and explores their strengths and weaknesses. He also examines the differences between integration of a sensor
“Embedded Vision Made Smart: Introduction to the HALCON Embedded Machine Vision Library,” a Presentation from MVTec
Olaf Munkelt, Co-founder and Managing Director at MVTec Software GmbH, presents the "Embedded Vision Made Smart: Introduction to the HALCON Embedded Machine Vision Library" tutorial at the May 2017 Embedded Vision Summit. In this presentation, Munkelt demonstrates how easy it is to develop an embedded vision (identification) application based on the HALCON Embedded standard software
“Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles,” a Presentation from NXP Semiconductors
Ali Osman Ors, Director of Automotive Microcontrollers and Processors at NXP Semiconductors, presents the "Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles" tutorial at the May 2017 Embedded Vision Summit. A diverse set of sensor technologies is available and emerging to provide vehicle autonomy or driver assistance. These sensor technologies often
“Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles,” a Presentation from NXP Semiconductors
Ali Osman Ors, Director of Automotive Microcontrollers and Processors at NXP Semiconductors, presents the "Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles" tutorial at the May 2017 Embedded Vision Summit. A diverse set of sensor technologies is available and emerging to provide vehicle autonomy or driver assistance. These sensor technologies often
“Implementing an Optimized CNN Traffic Sign Recognition Solution,” a Presentation from NXP Semiconductors and Au-Zone Technologies
Rafal Malewski, Head of the Graphics Technology Engineering Center at NXP Semiconductors, and Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, present the "Implementing an Optimized CNN Traffic Sign Recognition Solution" tutorial at the May 2017 Embedded Vision Summit. Now that the benefits of using deep neural networks for image classification are well known, the
“Implementing an Optimized CNN Traffic Sign Recognition Solution,” a Presentation from NXP Semiconductors and Au-Zone Technologies
Rafal Malewski, Head of the Graphics Technology Engineering Center at NXP Semiconductors, and Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, present the "Implementing an Optimized CNN Traffic Sign Recognition Solution" tutorial at the May 2017 Embedded Vision Summit. Now that the benefits of using deep neural networks for image classification are well known, the
May 2017 Embedded Vision Summit Slides
The Embedded Vision Summit was held on May 1-3, 2017 in Santa Clara, California, as a 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 2017 Embedded Vision Summit
May 2016 Embedded Vision Summit Proceedings
The Embedded Vision Summit was held on May 2-4, 2016 in Santa Clara, California, as a educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations presented at the Summit are listed below. All of the slides from these presentations are included in… May 2016 Embedded Vision Summit
“Techniques for Efficient Implementation of Deep Neural Networks,” a Presentation from Stanford
Song Han, graduate student at Stanford, delivers the presentation "Techniques for Efficient Implementation of Deep Neural Networks" at the March 2016 Embedded Vision Alliance Member Meeting. Song presents recent findings on techniques for the efficient implementation of deep neural networks.
Deep Learning Use Cases for Computer Vision (Download)
Six Deep Learning-Enabled Vision Applications in Digital Media, Healthcare, Agriculture, Retail, Manufacturing, and Other Industries The enterprise applications for deep learning have only scratched the surface of their potential applicability and use cases. Because it is data agnostic, deep learning is poised to be used in almost every enterprise vertical… Deep Learning Use Cases for
Using Convolutional Neural Networks for Image Recognition
This article was originally published at Cadence's website. It is reprinted here with the permission of Cadence. Convolutional neural networks (CNNs) are widely used in pattern- and image-recognition problems as they have a number of advantages compared to other techniques. This white paper covers the basics of CNNs including a description of the various layers
“Harman’s Augmented Navigation Platform—The Convergence of ADAS and Navigation,” a Presentation from Harman
Alon Atsmon, Vice President of Technology Strategy at Harman International, presents the "Harman’s Augmented Navigation Platform—The Convergence of ADAS and Navigation" tutorial at the May 2015 Embedded Vision Summit. Until recently, advanced driver assistance systems (ADAS) and in-car navigation systems have evolved as separate standalone systems. Today, however, the combination of available embedded computing power
