AMD

“The OpenVX Computer Vision and Neural Network Inference Library Standard for Portable, Efficient Code,” a Presentation from AMD

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Radhakrishna Giduthuri, Software Architect at Advanced Micro Devices (AMD), presents the “OpenVX Computer Vision and Neural Network Inference Library Standard for Portable, Efficient Code” tutorial at the May 2018 Embedded Vision Summit. OpenVX is an industry-standard computer vision and neural network inference API designed for efficient implementation on a variety of embedded platforms. The API […]

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Computer Vision in Surround View Applications

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The ability to "stitch" together (offline or in real-time) multiple images taken simultaneously by multiple cameras and/or sequentially by a single camera, in both cases capturing varying viewpoints of a scene, is becoming an increasingly appealing (if not necessary) capability in an expanding variety of applications. High quality of results is a critical requirement, one

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Visual Intelligence Opportunities in Industry 4.0

Algorithms, AMD, Articles, Basler, Edge AI and Vision Alliance, Industrial Vision (Computer Vision), MVTec, Processors, Sensors and Cameras, Software, Technical Articles, Tools /

In order for industrial automation systems to meaningfully interact with the objects they're identifying, inspecting and assembling, they must be able to see and understand their surroundings. Cost-effective and capable vision processors, fed by depth-discerning image sensors and running robust software algorithms, continue to transform longstanding industrial automation aspirations into reality. And, with the emergence

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Figure2

Software Frameworks and Toolsets for Deep Learning-based Vision Processing

Algorithms, AMD, Articles, Au-Zone Technologies, BDTI, Edge AI and Vision Alliance, MVTec, Processors, Software, Synopsys, Technical Articles, Tools /

This article provides both background and implementation-detailed information on software frameworks and toolsets for deep learning-based vision processing, an increasingly popular and robust alternative to classical computer vision algorithms. It covers the leading available software framework options, the root reasons for their abundance, and guidelines for selecting an optimal approach among the candidates for a

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“OpenCV on Zynq: Accelerating 4k60 Dense Optical Flow and Stereo Vision,” a Presentation from Xilinx

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Nick Ni, Senior Product Manager for SDSoC and Embedded Vision at Xilinx, presents the "OpenCV on Zynq: Accelerating 4k60 Dense Optical Flow and Stereo Vision" tutorial at the May 2017 Embedded Vision Summit. OpenCV libraries are widely used for algorithm prototyping by many leading technology companies and computer vision researchers. FPGAs can achieve unparalleled compute

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Tend Secure Lynx A

Cloud-versus-Edge and Centralized-versus-Distributed: Evaluating Vision Processing Alternatives

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Although incorporating visual intelligence in your next product is an increasingly beneficial (not to mention practically feasible) decision, how to best implement this intelligence is less obvious. Image processing can optionally take place completely within the edge device, in a network-connected cloud server, or subdivided among these locations. And at the edge, centralized and distributed

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“Caffe to Zynq: State-of-the-Art Machine Learning Inference Performance in Less Than 5 Watts,” a Presentation from Xilinx

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Vinod Kathail, Distinguished Engineer and leader of the Embedded Vision team at Xilinx, presents the "Caffe to Zynq: State-of-the-Art Machine Learning Inference Performance in Less Than 5 Watts" tutorial at the May 2017 Embedded Vision Summit. Machine learning research is advancing daily with new network architectures, making it difficult to choose the best CNN algorithm

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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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“Semantic Segmentation for Scene Understanding: Algorithms and Implementations,” a Presentation from Auviz Systems

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Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit. 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

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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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