@inproceedings{d39680ec78ac49b3b115623003138bb2,
title = "A multi-feature integrated visual attention model for matching-area suitability analysis in visual navigation",
abstract = "For matching-area suitability analysis of unmanned aerial vehicle (UAV) visual navigation, a multi-feature integrated visual attention model (MFI-VAM) was established by introducing invariant features, based on which the extraction method of suitable matching-area was proposed. Speeded-up robust features (SURF) were added into visual attention model. The conspicuity map of SURF channel is obtained by cross-scale feature maps integration. Based on multi-feature fusion of SURF, color, intensity and orientation, the MFI-VAM model was built. Salient locations in current map were chosen as suitable matching-areas based on this model. Simulation results show that the image registration error of extracted matching-area based on proposed method is small. This paper could provide new ideas and theoretical guidance for UAV autonomous navigation.",
keywords = "Multi-feature Integrated, UAV, Visual Attention, Visual Navigation, matching-area suitability",
author = "Zhenlu Jin and Quan Pan and Chunhui Zhao and Huixia Liu and Wei Jia",
year = "2013",
month = oct,
day = "18",
language = "英语",
isbn = "9789881563835",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5122--5127",
booktitle = "Proceedings of the 32nd Chinese Control Conference, CCC 2013",
note = "32nd Chinese Control Conference, CCC 2013 ; Conference date: 26-07-2013 Through 28-07-2013",
}