SANTA CLARA, CA — At the Embedded Vision Summit, here, The Khronos Group (www.khronos.org), an open consortium of hardware and software companies, announced the availability of the OpenVX 1.1 specification for cross platform acceleration of computer vision applications and libraries.
OpenVX enables performance and power optimized computer vision algorithms for use cases such as face, body and gesture tracking, smart video surveillance, automatic driver assistance systems, object and scene reconstruction, augmented reality, visual inspection, and robotics.
Conformant OpenVX 1.0 implementations and tools are shipping from AMD, Imagination, Intel, Nvidia, Synopsis, and VeriSilicon. OpenVX 1.1 builds on this momentum by adding new processing functions for use cases such as computational photography, and enhances application control over how data is accessed and processed. An open source OpenVX 1.1 sample implementation and full conformance tests will be available in the first half of 2016. Details on the OpenVX specifications and Adopters Program are available at: www.khronos.org/openvx.
The precisely defined specification and conformance tests for OpenVX make it well suited for deployment in production systems where cross-vendor consistency and reliability are essential. Additionally, OpenVX is easily extensible to enable nodes to be deployed to meet customer needs, ahead of being integrated into the core specification.
The new OpenVX 1.1 specification is a significant expansion in the breadth and flexibility of vision processing functionality and the OpenVX graph framework:
- Definition and processing of Laplacian pyramids to support computational photography use cases
- Median, erode and dilate image filters, including custom patterns
- Easier and less error prone methods to read and write data to and from OpenVX objects
- Targets - to control on which accelerator to run nodes in a heterogeneous device
- More convenient and flexible API for extending OpenVX with user kernels
- Many other improvements and clarifications to infrastructure functions and vision nodes.