
OpenVX Programming Guide
Author(s): Frank Brill (Author), Victor Erukhimov (Author), Steve Ramm (Author), Radhakrishna Giduthuru (Author)
- Publisher: Academic Press
- Publication Date: 1 Jun. 2020
- Language: English
- Print length: 372 pages
- ISBN-10: 0128164255
- ISBN-13: 9780128164259
Book Description
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.
- Get to grips with the OpenVX standard and gain insight why various options were chosen
- Start developing efficient OpenVX code instantly
- Understand design principles and use them to create robust code
- Develop consumer and industrial products that use computer vision to understand and interact with the real world
Editorial Reviews
Review
Helps readers learn how to write fast and power-efficient OpenVX code on embedded systems
About the Author
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 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.
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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