Suitability analysis based on multi-feature fusion visual saliency model in vision navigation

Zhen Lu Jin, Quan Pan, Chun Hui Zhao, Yong Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Matching-area suitability analysis in vision navigation system for unmanned aerial vehicle (UAV) is a very worthy but full of challenges research area. In this paper, a multi-feature fusion based visual saliency model (MFF-VSM) was established by introducing invariant features of speeded-up robust features (SURF) directly into the visual saliency model, based on which the extraction method of suitable matching-areas was proposed. With the integration of cross-scale SURF feature maps in the way we defined, the conspicuity map of SURF channel is obtained. By adding SURF channel into the traditional visual saliency model and fusing multi-feature of SURF, color, intensity and orientation, the MFF-VSM model is proposed. Based on the MFF-VSM, salient locations in sensed map could be obtained and chosen as suitable matching-areas. Simulation results show that the error of image registration with extracted matching-areas based on MFF-VSM meet the demands of vision navigation system. The proposed method may provide new ideas for autonomous navigation of UAV in the future.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Information Fusion, FUSION 2013
Pages235-241
Number of pages7
StatePublished - 2013
Event16th International Conference of Information Fusion, FUSION 2013 - Istanbul, Turkey
Duration: 9 Jul 201312 Jul 2013

Publication series

NameProceedings of the 16th International Conference on Information Fusion, FUSION 2013

Conference

Conference16th International Conference of Information Fusion, FUSION 2013
Country/TerritoryTurkey
CityIstanbul
Period9/07/1312/07/13

Keywords

  • Multi-feature Fusion
  • Suitability Analysis
  • SURF
  • Vision Navigation
  • Visual Saliency Model

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