dc.contributor.author | Sun, Junding | |
dc.contributor.author | Fan, Guoliang | |
dc.contributor.author | Yu, Liangjiang | |
dc.contributor.author | Wu, Xiaosheng | |
dc.date.accessioned | 2019-08-28T16:00:52Z | |
dc.date.available | 2019-08-28T16:00:52Z | |
dc.date.issued | 2014-04-21 | |
dc.identifier | oksd_sun_concave-convexl_2014-04-21 | |
dc.identifier.citation | Sun, J., Fan, G., Yu, L., & Wu, X. (2014). Concave-convex local binary features for automatic target recognition in infrared imagery. EURASIP Journal on Image and Video Processing, 2014, Article 23. https://doi.org/10.1186/1687-5281-2014-23 | |
dc.identifier.uri | https://hdl.handle.net/11244/321381 | |
dc.description.abstract | This paper presents a novel feature extraction algorithm based on the local binary features for automatic target recognition (ATR) in infrared imagery. Since the inception of the local binary pattern (LBP) and local ternary pattern (LTP) features, many extensions have been proposed to improve their robustness and performance in a variety of applications. However, most attentions were paid to improve local feature extraction with little consideration on the incorporation of global or regional information. In this work, we propose a new concave-convex partition (CCP) strategy to improve LBP and LTP by dividing local features into two distinct groups, i.e., concave and convex, according to the contrast between local and global intensities. Then two separate histograms built from the two categories are concatenated together to form a new LBP/LTP code that is expected to better reflect both global and local information. Experimental results on standard texture images demonstrate the improved discriminability of the proposed features and those on infrared imagery further show that the proposed features can achieve competitive ATR results compared with state-of-the-art methods. | |
dc.format | application/pdf | |
dc.language | en_US | |
dc.publisher | SpringerOpen | |
dc.rights | This material has been previously published. In the Oklahoma State University Library's institutional repository this version is made available through the open access principles and the terms of agreement/consent between the author(s) and the publisher. The permission policy on the use, reproduction or distribution of the material falls under fair use for educational, scholarship, and research purposes. Contact Digital Resources and Discovery Services at lib-dls@okstate.edu or 405-744-9161 for further information. | |
dc.title | Concave-convex local binary features for automatic target recognition in infrared imagery | |
osu.filename | oksd_sun_concave-convexl_2014-04-21.pdf | |
dc.description.peerreview | Peer reviewed | |
dc.identifier.doi | 10.1186/1687-5281-2014-23 | |
dc.description.department | Electrical and Computer Engineering | |
dc.type.genre | Article | |
dc.type.material | Text | |
dc.subject.keywords | local binary pattern | |
dc.subject.keywords | local ternary pattern | |
dc.subject.keywords | automatic target recognition | |
dc.subject.keywords | concave-convex partition | |