OpenVL is the future of developer-friendly computer vision - existing vision frameworks provide access at a very low level, such as individual algorithm names (often named after their inventor), while OpenVL provides a higher-level abstraction to hide the details of sophisticated vision techniques: developers use a task-centred API to supply a description of the problem, and OpenVL interprets the description and provides a solution.

The OpenVL computer vision abstraction will support hardware acceleration and multiple platforms (mobile, cloud, desktop, console), and therefore also allows vendor-specific implementations. We are committed to making it an open API available to everyone (and hope to make it an open standard); Continue reading...
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Towards OpenVL: Improving Real-Time Performance of Computer Vision Applications
Changsong Shen, James J. Little and Sidney Fels
Meeting constraints for real-time performance is a main issue for computer vision, especially for embedded computer vision systems. This chapter presents our progress on our open vision library (OpenVL), a novel software archi- tecture to address efficiency through facilitating hardware acceleration, reusabil- ity, and scalability for computer vision systems. A logical image understanding pipeline is introduced to allow parallel processing. We also discuss progress on our middleware—vision library utility toolkit (VLUT)—that enables applications to operate transparently over a heterogeneous collection of hardware implementa- tions. OpenVL works as a state machine, with an event-driven mechanism to provide users with application-level interaction. Various explicit or implicit synchronization and communication methods are supported among distributed processes in the log- ical pipelines. The intent of OpenVL is to allow users to quickly and easily recover useful information from multiple scenes, in a cross-platform, cross-language man- ner across various software environments and hardware platforms. To validate the critical underlying concepts of OpenVL, a human tracking system and a local po- sitioning system are implemented and described. The novel architecture separates the specification of algorithmic details from the underlying implementation, allow- ing for different components to be implemented on an embedded system without recompiling code.
    author = {Changsong Shen and James J. Little and Sidney Fels},
    title = {Embedded Computer Vision},
    chapter = {Towards OpenVL: Improving Real-Time Performance of Computer Vision Applications},
    series = {Advances in Computer Vision and Pattern Recognition},
    edition = {1st},
    pages = {195--218},
    year = {2008},
    editor = {Branislav Kisacanin and Shuvra S. Bhattacharyya and Sek Chai},
    publisher = {Springer},
    address = {Berlin / Heidelberg, Germany},
    isbn = {978-1-84800-303-3},
    doi = {},
    url = {}