Bookmark and Share

OpenVX Implementations Deliver Robust Computer Vision Applications

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:


Key to the widespread adoption of embedded vision is the ease of developing software that runs efficiently on a diversity of hardware platforms, with high performance, low power consumption and cost-effective system resource needs. In the past, this combination of objectives has been a tall order, since it has historically required significant code optimization for particular device architectures, thereby hampering portability to other architectures. Fortunately, this situation is changing with the maturation of the OpenVX standard created and maintained by the Khronos Group. This article provides implementation details of several design examples that leverage various capabilities of the standard.

OpenVX, an API from the Khronos Group, is an open standard for developing computer vision applications that are portable to a wide variety of computing platforms. It uses the concept of a computation graph to abstract the compute operations and data movement required by an algorithm, so that a broad range of hardware options can be used to execute the algorithm. An OpenVX implementation targeting a particular hardware platform translates the graph created by the application programmer into the instructions needed to execute efficiently on that hardware. Such flexibility means that the programmer will not need to rewrite his or her code when re-targeting new hardware, or to write new code specific to that hardware, making OpenVX a cross-platform API.

A previously published article in this series covered the initial v1.0 OpenVX specification and provided an overview of the standard's objectives, along with an explanation of its capabilities, as they existed in early 2016. This follow-on article showcases several case study examples of OpenVX implementations in various applications, leveraging multiple hardware platforms along with both traditional and deep learning computer vision algorithms. And it introduces readers to an industry alliance created to help product creators incorporate practical computer vision capabilities into their hardware and software, along with outlining the technical resources that this alliance provides (see sidebar "Additional Developer Assistance").

A companion article focuses on more recent updates to the OpenVX API, up to and including latest v1.2 of the specification and associated conformance tests, along with the recently published set of extensions that OpenVX implementers can optionally provide. It...