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TOD Performance Model for Staring Thermal Imager with Machine Vision

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Abstract

A triangle orientation discrimination threshold(TOD) performance theoretical model is derived for the staring thermal imager based on machine vision. Specifically, how to obtain the TOD curve based on machine vision is briefly described. The spatial frequency distribution of the triangle test pattern is first determined. The transform and response characteristics of the non-periodic triangle pattern and its background clutter through machine vision-based thermal imager are analyzed. The three-dimensional matched filter is adopted to characterize quantitatively the spatial and temporal integration of image enhancement algorithms to the output triangle pattern signal, various noise components and background clutter, and the signal-to-interference ratio (SIR) of the triangle pattern output image is derived for the staring thermal imager based on machine vision. Then, the TOD performance theoretical model is established by assuming that the output SIR is equal to the threshold SIR75% determined by the discrimination criteria of machine vision. Preliminary simulation and experimental results show that this theoretical model can give reasonable prediction of the TOD performance curve for staring thermal imagers based on machine vision.

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References

  1. P. Bijl and J.M. Valeton, TOD, a new method to characterize electro-optical system performance, Proc. SPIE. 3377, 182–193 (1998).

    Google Scholar 

  2. P. Bijl and J.M. Valeton, TOD, the alternative to MRTD and MRC, Opt. Eng. 37(7), 1984–1994 (1998).

    Google Scholar 

  3. R. G. Driggers, V. Hodgkin, R. Vollmerhausen and P. O’Shea, Minimum resolvable temperature difference measurement on undersampled imagers. Proc. SPIE. 5076, 179–189 (2003).

    Google Scholar 

  4. P. Bijl and J. M. Valeton, Guidelines for accurate TOD measurement. Proc. SPIE. 3701, 14–25 (1999).

    Google Scholar 

  5. J. Kostrzewa, J. Long and J. Graff et al., TOD versus MRT when evaluating thermal imagers that exhibit dynamic performance. Proc. SPIE. 5076, 220–232 (2003).

    Google Scholar 

  6. P. Bijl, M. A. Hogervorst and J. M. Valeton, TOD, NVTherm and TRMD model calculation: a comparison[J]. Proc. SPIE. 4719, 51–62 2002.

  7. P. Bijl, J. M. Valeton, Validation of the new triangle orientation discrimination method and ACQUIRE model predictions using observer performance data for ship targets. Opt. Eng. 37(7), 1984–1994 (1999).

    Google Scholar 

  8. P. Bijl, J. M. Valeton and A. N. de Jong, TOD predicts target acquisition performance and scanning thermal imager, Proc. SPIE. 4030, 96–103 (2000).

    Google Scholar 

  9. M. A. Hogervorst, P. Bijl and J. M. Valeton, Capturing the sampling effects: a TOD sensor performance model. Proc. SPIE. 4372, 62–73 (2001).

    Google Scholar 

  10. W. Xiao-rui, Z. Jian-qi, F. Zhuo-xiang and C. Hong-hua, “Equation-based triangle orientation discrimination sensor performance model based on sampling effects,” Applied Optics 44(4), 498–505 (2005).

    Google Scholar 

  11. J. D. McGlynn and D. J. Sofianos, Parametric Model-Based Characterization of IR Clutter, SPIE Proceedings, 2470, 236–244 1995.

  12. C. Wigren, A Generic IRST Detection Performance Model. SPIE Proceedi-ng, 4030, 206–217 2000.

  13. J. D’ Agostino and C. Webb, 3-D analysis framework and measurement methodology for imaging system noise, Proc. SPIE. 1488, 110–121 (1991).

  14. G. C. Holst, Testing and Evaluation of Infrared Imaging Systems. America: JCD Publishing and SPIE Optical Engineering Press, Second Edition, 371–374 (2000)

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Acknowledgements

This work was sponsored by Research Funds for the Doctoral Program of Higher Education(K50107050224) and Aviation science funds(20070181005). We express our appreciations for their support.

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Authors and Affiliations

  1. School of Technical Physics, Xidian University, Xi’an, Shaanxi, 710071, China

    Yingxia Wu, Xiao-rui Wang & Jianqi Zhang

  2. Aviation Science National Key Laboratory, Luoyang, Henan, 210065, China

    Hongjie Lin

Authors
  1. Yingxia Wu
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  2. Xiao-rui Wang
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  3. Hongjie Lin
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  4. Jianqi Zhang
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Corresponding author

Correspondence to Xiao-rui Wang.

Additional information

This project was supported by Key Project of Chinese Ministry of Education(109143), Research Funds for the Doctoral Program of Higher Education(20070701020) and Aviation science funding( 20070181005)

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Wu, Y., Wang, Xr., Lin, H. et al. TOD Performance Model for Staring Thermal Imager with Machine Vision. J Infrared Milli Terahz Waves 31, 13–23 (2010). https://doi.org/10.1007/s10762-009-9553-z

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  • DOI: https://doi.org/10.1007/s10762-009-9553-z

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