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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Developer-Friendly Segmentation using OpenVL, a High-Level Task-Based Abstraction
Gregor Miller, Daesik Jang and Sidney Fels
Research into computer vision techniques has far outpaced the development of interfaces (such as APIs) to support the techniques' accessibility, especially to developers who are not experts in the field. We present a new interface, specifically for segmentation methods, designed to be application-developer-friendly while retaining sufficient power and flexibility to solve a wide variety of problems. The interface presents segmentation at a higher level (above algorithms) and uses a task-based description derived from definitions of low-level segmentation. We show that through interpretation, the description can be used to invoke an appropriate method to provide the developer's requested result. Our proof-of-concept implementation interprets the model description and invokes one of six segmentation methods with automatically derived parameters, which we demonstrate on a range of segmentation tasks. We also discuss how the concepts presented for segmentation may be extended to other computer vision problems.

Presented at the IEEE Workshop on User-Centred Computer Vision at the Winter Vision Meetings in Tampa, January 2013.
BibTeX
@InProceedings{Miller:WACV2013,
    author = {Gregor Miller and Sidney Fels},
    title = {OpenVL: A Task-Based Abstraction for Developer-Friendly Computer Vision},
    booktitle = {Proceedings of the 13th IEEE Workshop on the Applications of Computer Vision (WACV)},
    series = {WVM'13},
    pages = {288--295},
    month = {January},
    year = {2013},
    publisher = {IEEE},
    address = {New York City, New York, U.S.A.},
    isbn = {978-1-4673-5053-2},
    location = {Tampa, Florida, U.S.A.},
    doi = {http://dx.doi.org/10.1109/WACV.2013.6475031},
    url = {http://www.openvl.org/papers.php?id=Miller:UCCV2013}
}