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Proceedings Paper

Aircraft type recognition based on convex hull features and SVM
Author(s): Yuan Liu; Xiuqin Wu; Richang Hong
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Paper Abstract

Most current algorithms of aircraft type recognition are based on the binary images which are obtained by utilizing the technology of image segmentation. Thus the effect of image segmentation will influence the sequent classification to a great extent. Moreover, image segmentation in complex background remains a challenging research area. In our work, we propose a novel aircraft type recognition algorithm based on the aircrafts' convex hull features and Support Vector Machine (SVM). We first obtain the aircrafts' external contours while removing background. And then, we compute the planar convex hulls of the external contours. Based on the convex hulls, we combine the characteristics unique to the aircraft object, to introduce an extracting method of major symmetry axle and corresponding characters. Finally, we select the SVM which has high generalization capabilities and high performance in tackling small sample size in the pattern classification task to perform the classification. Experiment results show that the convex hull feature of aircraft object is approximately invariant, and can successfully eliminate the need to segment the object region from the complex background. The aircraft type recognition is efficient and feasible, and especially applicable for raw gray images.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863Q (15 November 2007); doi: 10.1117/12.750439
Show Author Affiliations
Yuan Liu, Univ. of Science and Technology of China (China)
Xiuqin Wu, Univ. of Science and Technology of China (China)
Richang Hong, Univ. of Science and Technology of China (China)

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)

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