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An Abstraction for Correspondence Search Using Task-Based Controls

  • Gregor Miller
  • Sidney Fels
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9009)

Abstract

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.

Keywords

Computer Vision Feature Descriptor Correspondence Problem Image Width Blur Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We would like to gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Graphics, Animation and New Media Network of Centres of Excellence (GRAND NCE).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Human Communication Technologies LaboratoryUniversity of British ColumbiaVancouverCanada

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