The correspondence problem (finding matching regions in images) is a fundamental task in computer vision. While the concept is simple, the complexity of feature detectors and descriptors has increased as they provide more efficient and higher quality correspondences. This complexity is a barrier to developers or system designers who wish to use computer vision correspondence techniques within their applications. We have designed a novel abstraction layer which uses a task-based description (covering the conditions of the problem) to allow a user to communicate their requirements for the correspondence search. This is mainly based on the idea of variances which describe how sets of images vary in blur, intensity, angle, etc. Our framework interprets the description and chooses from a set of algorithms those that satisfy the description. Our proof-of-concept implementation demonstrates the link between the description set by the user and the result returned. The abstraction is also at a high enough level to hide implementation and device details, allowing the simple use of hardware acceleration.

Presented at the 2nd Workshop on User-Centred Computer Vision with the Asian Conference on Computer Vision in Singapore, November 2014.