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VisionSystemsDesign

“Recent Developments in Embedded Vision: Algorithms, Processors, Tools and Applications,” a Presentation from the Embedded Vision Alliance

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On January 25, 2017, Embedded Vision Alliance founder Jeff Bier delivered the webinar “Recent Developments in Embedded Vision: Algorithms, Processors, Tools and Applications”, in partnership with Vision Systems Design. Bier’s presentation is available for download here (3.6 MB PDF). For more information and to view the free on-demand archive of… “Recent Developments in Embedded Vision: […]

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“Image and Video Summarization,” a Presentation from the University of Washington

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Professor Jeff Bilmes of the University of Washington delivers the presentation "Image and Video Summarization" at the December 2016 Embedded Vision Alliance Member Meeting. Bilmes provides an overview of the state of the art in image and video summarization.

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“Deploying Embedded Vision for Retail Analytics,” a Presentation from RetailNext

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RetailNext's Mark Jamtgaard, Director of Technology, and Bill Adamec, Research and Development Manager, deliver the presentation "Deploying Embedded Vision for Retail Analytics" at the December 2016 Embedded Vision Alliance Member Meeting. Jamtgaard and Adamec Chehebar explain how retailers are using embedded vision to optimize store layout and staffing based on measured customer behavior at scale.

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“Using Vision to Improve Waste Collection Efficiency,” a Presentation from Compology

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Ben Chehebar, co-founder of Compology, delivers the presentation "Using Vision to Improve Waste Collection Efficiency" at the December 2016 Embedded Vision Alliance Member Meeting. Chehebar describes a novel vision-based solution that is dramatically improving the efficiency of trash collection.

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Facial Analysis Delivers Diverse Vision Processing Capabilities

Algorithms, Articles, Automotive, Edge AI and Vision Alliance, Entertainment, Face Detection, Face Recognition, Medical, NXP Semiconductors, Processors, Retail, Security, Sensors and Cameras, Software, Synopsys, Technical Articles, Tools, Xperi /

Computers can learn a lot about a person from their face – even if they don’t uniquely identify that person. Assessments of age range, gender, ethnicity, gaze direction, attention span, emotional state and other attributes are all now possible at real-time speeds, via advanced algorithms running on cost-effective hardware. This article provides an overview of

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Figure8

Camera Interfaces Evolve to Address Growing Vision Processing Needs

Allied Vision, AMD, Articles, Basler, Edge AI and Vision Alliance, Processors, Sensors and Cameras, Technical Articles, videantis /

Before a still image or video stream can be analyzed, it must first be captured and transferred to the processing subsystem. Cameras, along with the interfaces that connect them to the remainder of the system, are therefore critical aspects of any computer vision design. This article provides an overview of camera interfaces, and discusses their

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Figure5

Deep Learning with INT8 Optimization on Xilinx Devices

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This is a reprint of a Xilinx-published white paper which is also available here (1 MB PDF). Xilinx INT8 optimization provide the best performance and most power efficient computational techniques for deep learning inference. Xilinx's integrated DSP architecture can achieve 1.75X solution-level performance at INT8 deep learning operations than other FPGA DSP architectures. ABSTRACT The

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

Algorithms, Edge AI and Vision Alliance, Face Detection, Face Recognition, Object Identification, Object Tracking, Processors, Security, Sensors and Cameras, Videos /

Nik Gagvani, President and General Manager of CheckVideo, delivers the presentation "Intelligent Video Surveillance: Are We There Yet?" at the September 2016 Embedded Vision Alliance Member Meeting. Gagvani provides an insider's perspective on vision-enabled video surveillance applications.

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“Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks,” a Presentation from MIT

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Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning. Followup: per Professor Sze, "Slides available at: http://www.rle.mit.edu/eems/wp-content/uploads/2016/07/Sze-Energy-Efficient-Hardware-for-Embedded-Vision-and-Deep-Learning-CVPR-2016-EVW.pdf".

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“Enabling Efficient Heterogeneous Processing Through Coherency,” a Presentation from the HSA Foundation

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Dr. John Glossner, President of the HSA Foundation and CEO of GPT-US, delivers the presentation "Enabling Efficient Heterogeneous Processing Through Coherency" at the September 2016 Embedded Vision Alliance Member Meeting. Glossner describes the organization's goals and deliverables for enabling heterogeneous programming.

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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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