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Proceedings Paper

Sequence IR images background estimation algorithm based on kernel exponential weighted least squares
Author(s): Bin Zhu; Xiang Fan; Donghui Ma
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Paper Abstract

Background estimation plays an essential role in many infrared (IR) target detection algorithms. A kernel-based background estimation algorithm for stationary camera is proposed in this paper. The nonlinear version of least squares (LS) algorithm: kernel least squares (KLS) and its exponential weighted form (KEWLS) are deduced use kernel methods (KMs). The background of IR image is estimated by KLS or KEWLS nonlinear regression utilize sequence images as training set; then targets are segmented by threshold dependent techniques in the difference image. Experiments of nonlinear function regression and IR image background estimation are performed. The results of these experiments are compared to that of LS algorithm, a single-frame and a multi-frame background estimation algorithm. The feasibility of nonlinear function regression and background estimation via kernelized LS is thus demonstrated.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74951Y (30 October 2009); doi: 10.1117/12.832993
Show Author Affiliations
Bin Zhu, Electronic Engineering Institute (China)
Xiang Fan, Electronic Engineering Institute (China)
Donghui Ma, Electronic Engineering Institute (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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