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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OpenVL: Abstracting Vision Tasks Using a Segment-Based Language Model
Gregor Miller, Steve Oldridge and Sidney Fels
Computer vision is a complex field which can be challenging for those outside the community to apply in the real world. In this paper we show how to provide access to sophisticated computer vision methods to general developers, hobbyists or researchers outside the field. Our contribution is an abstraction used to describe images, local image conditions and between-image conditions using segments as a basis. We illustrate how a descriptive language model can be built on the segment to provide an intuitive mental model of computer vision to mainstream developers. We then demonstrate how we can map a description of the task composed of the segment-based language into the space of algorithms in order to choose an appropriate method to solve the problem. We use the problems of segmentation, correspondence and image registration to show how end-to-end problems may be constructed using our novel metaphor.

Presented in Regina, May 2013 at the International Conference on Computer and Robot Vision.
    author = {Gregor Miller and Steve Oldridge and Sidney Fels},
    title = {OpenVL: Abstracting Vision Tasks Using a Segment-Based Language Model},
    booktitle = {Proceedings of the 10th International Conference on Computer and Robot Vision},
    series = {CRV'13},
    pages = {},
    month = {May},
    year = {2013},
    publisher = {IEEE},
    address = {New York City, New York, U.S.A.},
    isbn = {},
    organization = {CIPPRS},
    location = {Regina, Saskatchewan, Canada},
    doi = {},
    url = {}