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Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors

By Guohua Lv, Shyh Teng and Guojun Lu

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

Topics: 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Science, Discriminative power, Gradient information, Image registration, SIFT-Like descriptors
Publisher: Elsevier B.V.
Year: 2016
DOI identifier: 10.1016/j.patrec.2016.09.011
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