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Keywords = soft concave-convex partition (SCCP)

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20 pages, 947 KiB  
Article
Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery
by Xiaosheng Wu, Junding Sun, Guoliang Fan and Zhiheng Wang
Sensors 2015, 15(3), 6399-6418; https://doi.org/10.3390/s150306399 - 16 Mar 2015
Cited by 23 | Viewed by 7806
Abstract
This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. [...] Read more.
This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods. Full article
(This article belongs to the Section Physical Sensors)
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