Abstract
There has been significant research into the development
of visual feature detectors and descriptors that are robust to a number of
image deformations. Some of these methods have emphasized the need to
improve on computational speed and compact representations so that they
can enable a range of real-time applications with reduced computational
requirements. In this paper we present modified detectors and descriptors
based on the recently introduced CenSurE, and show
experimental results that aim to highlight the computational savings that
can be made with limited reduction in performance. The developed methods
are based on exploiting the concept of sparse sampling which may be of
interest to a range of other existing approaches.
| Translated title of the contribution | SUSurE: Speeded Up Surround Extrema Feature Detector and Descriptor for Realtime Applications |
|---|---|
| Original language | English |
| Title of host publication | "Workshop on Feature Detectors and Descriptors: The State Of The Art and Beyond" as part of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2009 |
| Publication status | Published - 2009 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: "Workshop on Feature Detectors and Descriptors: The State Of The Art and Beyond" as part of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2009
Other identifier: 2001018