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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Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers
Daesik Jang, Gregor Miller and Sidney Fels
The majority of computer vision research focusses on advancing technical methods for image analysis, with a coupled increase in complexity and sophistication. The problem of providing access to these sophisticated techniques is largely ignored, leading to a lack of application by mainstream applications. We present a feature-based clustering segmentation algorithm with novel modifications to fit a developer-centred abstraction. This abstraction acts as an interface which accepts a description of segmentation in terms of properties (colour, intensity, texture, etc.), constraints (size, quantity) and priorities (biasing a segmentation). This paper discusses the modifications needed to fit the algorithm into the abstraction, which conditions of the abstraction it supports, and results of the various conditions demonstrating the coverage of the segmentation problem space. The algorithm modification process is discussed generally to help other researchers mould their algorithms to similar abstractions.

Presented at the International Workshop on Developer-Centred Computer Vision at the Asian Conference on Computer Vision in Daejeon, November 2012.
    author = {Daesik Jang and Gregor Miller and Sidney Fels},
    title = {Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers},
    booktitle = {Proceedings of the 1st International Workshop on Developer-Centred Computer Vision},
    series = {ACCV'12},
    pages = {254--265},
    month = {November},
    year = {2012},
    publisher = {Springer},
    address = {Berlin / Heidelberg, Germany},
    isbn = {978-3-642-37409-8},
    location = {Daejeon, Korea},
    doi = {},
    url = {}