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Infrared Image Simulation Based On Statistical Learning Theory

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Abstract

A real-time simulation algorithm of infrared image based on statistical learning theory is presented. The method includes three contents to achieve real-time simulation of infrared image, such as acquiring the training sample, forecasting the scene temperature field value by statistical learning machine, data processing and data analysis of temperature field. The simulation result shows this algorithm based on ν - support vector regression have better maneuverability and generalization than the other method, and the simulation precision and real-time quality are satisfying.

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Reference

  1. H.-P. Wu, Research into theoretical calculation method on engineering of transmitance of infrared radiation through atmosphere[J]. Optics and Precision Engineering 6(4), 35–43 (1998).

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Authors and Affiliations

  1. Key Lab of Infrared and Low Temperature Plasma of Anhui Province, Hefei Electronic Engineering Institute, Hefei, Anhui, 230037, People’s Republic of China

    Huang Chaochao, Wu Xiaodi & Tong Wuqin

Authors
  1. Huang Chaochao
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  2. Wu Xiaodi
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  3. Tong Wuqin
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Correspondence to Huang Chaochao.

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Chaochao, H., Xiaodi, W. & Wuqin, T. Infrared Image Simulation Based On Statistical Learning Theory. Int J Infrared Milli Waves 28, 1143–1153 (2007). https://doi.org/10.1007/s10762-007-9270-4

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  • DOI: https://doi.org/10.1007/s10762-007-9270-4

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