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OpenVX Programming Guide

  • ID: 4829301
  • Book
  • May 2020
  • Elsevier Science and Technology

OpenVX is the computer vision API adopted by many high-performance processor vendors. It is quickly becoming the preferred way to write fast and power-efficient code on embedded systems. OpenVX Programming Guidebook presents definitive information on OpenVX 1.2 and 1.3, the Neural Network, and other extensions as well as the OpenVX Safety Critical standard.

This book gives a high-level overview of the OpenVX standard, its design principles, and overall structure. It covers computer vision functions and the graph API, providing examples of usage for the majority of the functions. It is intended both for the first-time user of OpenVX and as a reference for experienced OpenVX developers.

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1. Introduction 2. Build your first OpenVX program 3. Using the Graph API to write efficient portable code 4. Building an OpenVX graph 5. Deploying an OpenVX graph to a target platform 6. Basic image transformations 7. Background subtraction and object detection 8. Computational photography 9. Efficient data input/output 10. Tracking 11. Use OpenVX for deep neural networks 12. OpenVX safety critical applications 13. Using OpenVX with other vision frameworks 14. Making the most of your OpenVX code

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Frank Brill Design Engineering Director, Cadence.

Frank Brill manages OpenVX software development for Cadence's Tensilica Imaging and Vision DSP organization. Frank obtained his PhD in Computer Science from the University of Virginia and started his career doing computer vision research and development for video security and surveillance applications at Texas Instruments, where he obtained 5 patents related to this work. He then moved into silicon device program management, where he was responsible for several digital still camera and multimedia chips, including the first device in TI's DaVinci line of multimedia processors (the DM6446). Frank worked at NVIDIA from 2013 to 2014, where he managed the initial development of NVIDIA OpenVX-based VisionWorks toolkit, and then worked at Samsung from 2014 to 2016, where he managed a computer vision R&D team in Samsung Mobile Processor Innovation Lab.
Victor Erukhimov CEO, itSeez3D.

Victor Erukhimov is a CEO of itSeez3D, the company that democratized 3D scanning. He also cofounded Itseez, the company that focused on developing computer vision solutions running on embedded platforms, specifically automotive safety systems. He held the positions of CTO, CEO, and President at Itseez, before the company was acquired by Intel Corporation in 2016. Victor was the chair of the OpenVX working group in 2012--2016, creating the standard for cross-platform computer vision API.
Radhakrishna Giduthuri Radhakrishna Giduthuri, Principal Engineer.

Radhakrishna Giduthuri is currently a principal engineer at Intel, focusing on software architecture for Intel AI Accelerators. Prior to working at Intel, he built computer vision, deep learning, and video compression software acceleration libraries for AMD GPUs & CPUs. He has an extensive background with software architecture, development, and performance tuning for various computer architectures ranging from general purpose DSPs, customizable DSPs, media processors, heterogeneous processors, GPUs, and several CPUs. He is the editor of the recent Khronos OpenVX specification documents.
Steve Ramm York University.

Stephen Ramm is currently a principal software engineer with Etas, a subsidiary of Bosch, where he is involved with Adaptive Autosar, working on reliable frameworks and development environments for advanced functionality in the automotive, rail, and other safety-critical industries. Until late 2017, he was Director of AI and Vision software at Imagination Technologies, where one of his responsibilities was the team producing an implementation of OpenVX accelerated by Imagination's GPU architecture.
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