HBD: Hexagon-Based Binary Descriptors

Liu, Y. and Siebert, J. P. (2016) HBD: Hexagon-Based Binary Descriptors. In: 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy, 27-29 Feb 2016, pp. 175-182. ISBN 9789897581755 (doi:10.5220/0005720401750182)

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In this paper, two new rotationally invariant hexagon-based binary descriptors (HBD), i.e., HexIDB and HexLDB, are proposed in order to obtain better feature discriminability while encoding less redundant information. Our new descriptors are generated based on a hexagonal grouping structure that improves upon the HexBinary descriptor we reported previously. The third level descriptors of HexIDB and HexLDB have 270 bits and 99 bits respectively fewer than that of SHexBinary, due to sampling ~61% fewer fields. Using learned parameters, HBD demonstrates better performance when matching the majority of the images in Mikolajczyk and Scmidt’s standard benchmark dataset, as compared to existing benchmark descriptors. Moreover, HBD also achieves promising level of performance when applied to pose estimation using the ALOI dataset, achieving ~0.5 pixels mean pose error, only slightly inferior to fixed-scale SIFT, but around 1.5 pixels better than standard SIFT.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Siebert, Dr Paul
Authors: Liu, Y., and Siebert, J. P.
College/School:College of Science and Engineering > School of Computing Science
Research Group:Computer Vision forAutonomous Systems
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