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A Regenerated Feature Extraction Method for Cross-modal Image Registration

  • Jian Yang
  • Qi WangEmail author
  • Xuelong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10989)

Abstract

Cross-modal image registration is an intractable problem in computer vision and pattern recognition. Inspired by that human gradually deepen to learn in the cognitive process, we present a novel method to automatically register images with different modes in this paper. Unlike most existing registrations that align images by single type of features or directly using multiple features, we employ the “regenerated” mechanism cooperated with a dynamic routing to adaptively detect features and match for different modal images. The geometry-based maximally stable extremal regions (MSER) are first implemented to fast detect non-overlapping regions as the primitive of feature regeneration, which are used to generate novel control-points using salient image disks (SIDs) operator embedded by a sub-pixel iteration. Then a dynamic routing is proposed to select suitable features and match images. Experimental results on optical and multi-sensor images show that our method has a better accuracy compared to state-of-the-art approaches.

Keywords

Feature regeneration MSER SIDs Image registration Dynamic routing 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL)Northwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.Unmanned System Research Institute (USRI)Northwestern Polytechnical UniversityXi’anPeople’s Republic of China
  3. 3.Xi’an Institute of Optics and Precision MechanicsChinese Academy of ScienceXi’anPeople’s Republic of China

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