- Title
- Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors
- Creator
- Lv, Guohua; Teng, Shyh; Lu, Guojun
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/104232
- Identifier
- vital:11040
- Identifier
-
https://doi.org/10.1016/j.patrec.2016.09.011
- Identifier
- ISSN:01678655
- Abstract
- In this paper we will investigate the gradient utilization in building SIFT (Scale Invariant Feature Transform)-like descriptors for image registration. There are generally two types of gradient information, i.e. gradient magnitude and gradient occurrence, which can be used for building SIFT-like descriptors. We will provide a theoretical analysis on the effectiveness of each of the two types of gradient information when used individually. Based on our analysis, we will propose a novel technique which systematically uses both types of gradient information together for image registration. Moreover, we will propose a strategy to select keypoint matches with a higher discrimination. The proposed technique can be used for both mono-modal and multi-modal image registration. Our experimental results show that the proposed technique improves registration accuracy over existing SIFT-like descriptors. © 2016 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Pattern Recognition Letters Vol. 84, no. (2016), p. 156-162
- Rights
- Copyright © 2016 Elsevier B.V.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; 1702 Cognitive Science; Discriminative power; Gradient information; Image registration; SIFT-Like descriptors
- Reviewed
- Hits: 1622
- Visitors: 1573
- Downloads: 1
| Thumbnail | File | Description | Size | Format |
|---|