LETTER FROM THE EDITOR Dear Colleague, TensorFlow has become a popular framework for creating machine learning-based computer vision applications, especially for the development of deep neural networks. If you’re planning to develop computer vision applications using deep learning and want to understand how to use TensorFlow to do it, then don’t miss an upcoming full-day, […]
Embedded Vision Insights: August 18, 2017 Edition Read More +
Jessica Gehlhar, Vision Solutions Engineer at Edmund Optics, presents the “Introduction to Optics for Embedded Vision” tutorial at the May 2017 Embedded Vision Summit. This talk provides an introduction to optics for embedded vision system and algorithm developers. Gehlhar begins by presenting fundamental imaging lens specifications and quality metrics. She explains key parameters and concepts
“Introduction to Optics for Embedded Vision,” a Presentation from Edmund Optics Read More +
Rudy Burger, Managing Partner at Woodside Capital Partners, presents the "What’s Hot? The M&A and Funding Landscape for Computer Vision Companies" tutorial at the May 2017 Embedded Vision Summit. The six primary markets driving computer vision are automotive, sports and entertainment, consumer and mobile, robotics and machine vision, medical, and security and surveillance. This presentation
Jay Yagnik, Head of Machine Perception Research at Google, presents the "Rapid Evolution and Future of Machine Perception" tutorial at the May 2017 Embedded Vision Summit. With the advent of deep learning, our ability to build systems that derive insights from perceptual data has increased dramatically. Perceptual data dwarfs almost all other data sources in
“The Rapid Evolution and Future of Machine Perception,” a Presentation from Google Read More +
COMPUTER VISION FOR IMAGE UNDERSTANDING Semantic Segmentation for Scene Understanding: Algorithms and Implementations Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very
Embedded Vision Insights: August 1, 2017 Edition Read More +
COMPUTER VISION FOR IMAGE UNDERSTANDING Semantic Segmentation for Scene Understanding: Algorithms and Implementations Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very
Embedded Vision Insights: August 1, 2017 Edition Read More +
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
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
Pete Warden, Google Research Engineer and technical lead on the company's mobile/embedded TensorFlow team, is a long-time advocate of the Embedded Vision Alliance. Warden has delivered presentations at both the 2016 ("TensorFlow: Enabling Mobile and Embedded Machine Intelligence") and 2017 ("Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP") Embedded Vision Summits, along with
Building Mobile Apps with TensorFlow: An Interview with Google’s Pete Warden Read More +
Pete Warden, Research Engineer at Google, presents the "Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP" tutorial at the May 2017 Embedded Vision Summit. TensorFlow is Google’s second-generation deep learning software framework. TensorFlow was designed from the ground up to enable efficient implementation of deep learning algorithms at different scales, from high-performance data
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