Sequence IR images background estimation algorithm based on kernel exponential weighted least squares

Bin Zhu; Xiang Fan; Donghui Ma

doi:10.1117/12.832993

30 October 2009 Sequence IR images background estimation algorithm based on kernel exponential weighted least squares

Bin Zhu, Xiang Fan, Donghui Ma

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74951Y (2009) https://doi.org/10.1117/12.832993
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Bin Zhu, Xiang Fan, and Donghui Ma "Sequence IR images background estimation algorithm based on kernel exponential weighted least squares", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74951Y (30 October 2009); https://doi.org/10.1117/12.832993

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