Abstract

The U. S. Army has adopted the targeting task performance (TTP) image quality metric for formal analyses of electro-optical imager performance. The most recent release of the model, NVIPM, changes the way TTP is calculated in order to improve the accuracy of facial identification. Predictions of the original and modified models are compared to both laboratory and field data. The change degrades NVIPM predictive accuracy for the tactical vehicle target set which is often used in hardware procurement specifications.

© 2016 Optical Society of America

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References

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  1. R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43(11), 2806–2818 (2004).
    [Crossref]
  2. R. Vollmerhausen and A. L. Robinson, “Modeling target acquisition tasks associated with security and surveillance,” Appl. Opt. 46(20), 4209–4221 (2007).
    [Crossref] [PubMed]
  3. R. H. Vollmerhausen, S. Moyer, K. Krapels, R. G. Driggers, J. G. Hixson, and A. L. Robinson, “Predicting the probability of facial identification using a specific object model,” Appl. Opt. 47(6), 751–759 (2008).
    [Crossref] [PubMed]
  4. R. H. Vollmerhausen, “Representing the observer in electro-optical target acquisition models,” Opt. Express 17, 017253 (2009).
  5. R. H. Vollmerhausen, R. G. Driggers, and D. L. Wilson, “Predicting range performance of sampled imagers by treating aliased signal as target-dependent noise,” J. Opt. Soc. Am. A 25(8), 2055–2065 (2008).
    [Crossref] [PubMed]
  6. R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The Targeting Task Performance (TTP) Metric; A new model for predicting target acquisition performance,” Technical Report AMSEL-NV-TR-230, U.S. Army CERDEC, Fort Belvoir, VA 22060 (2006).
  7. R. H. Vollmerhausen and T. Bui, “Using a targeting metric to predict the utility of an EO imager as a pilotage aid,” Proc. SPIE 6207, 62070C (2006).
    [Crossref]
  8. http://www.cerdec.army.mil/inside_cerdec/nvesd/integrated_performance_model/
  9. B. P. Teaney, D. M. Tomkinson, and J. G. Hixson, “Legacy modeling and range prediction comparison: NV-IPM versus SSCamIP and NVTherm,” Proc. SPIE 9452, 94520P (2015).
    [Crossref]
  10. R. H. Vollmerhausen, D. A. Reago, Jr., and R. G. Driggers, Analysis and Evaluation of Sampled Imaging Systems, Tutorial Texts in Optical Engineering Volume TT87, SPIE, Bellingham, WA (2010).
  11. P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 52940 (2004).
  12. R. J. Beaton and W. W. Farley, “Comparative study of the MTFA, ICS, and SQRI image quality metrics for visual display systems,” Armstrong Lab., Air Force Systems Command, Wright-Patterson AFB, OH, Report AL-TR-1992–0001, DTIC ADA252116 (1991).
  13. K. R. Boss and J. E. Lincoln, Engineering Data Compendium: Human Perception and Performance, Vol. 1, Harry G. Armstrong Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio (1988).
  14. V. Virsu and J. Rovamo, “Visual resolution, contrast sensitivity, and the cortical magnification factor,” Exp. Brain Res. 37(3), 475–494 (1979).
    [Crossref] [PubMed]
  15. C. R. Carlson, “Sine-wave threshold contrast-sensitivity function: dependence on display size,” RCA Review,” 43 (1982).
  16. S. Aghera, K. Krapels, J. Hixson, and R. G. Driggers, “Field verification of the Direct View Optics (DVO) Model for human facial identification,” Proc. SPIE 7343, 734311 (2009).
    [Crossref]

2015 (1)

B. P. Teaney, D. M. Tomkinson, and J. G. Hixson, “Legacy modeling and range prediction comparison: NV-IPM versus SSCamIP and NVTherm,” Proc. SPIE 9452, 94520P (2015).
[Crossref]

2009 (2)

S. Aghera, K. Krapels, J. Hixson, and R. G. Driggers, “Field verification of the Direct View Optics (DVO) Model for human facial identification,” Proc. SPIE 7343, 734311 (2009).
[Crossref]

R. H. Vollmerhausen, “Representing the observer in electro-optical target acquisition models,” Opt. Express 17, 017253 (2009).

2008 (2)

2007 (1)

2006 (1)

R. H. Vollmerhausen and T. Bui, “Using a targeting metric to predict the utility of an EO imager as a pilotage aid,” Proc. SPIE 6207, 62070C (2006).
[Crossref]

2004 (2)

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43(11), 2806–2818 (2004).
[Crossref]

P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 52940 (2004).

1979 (1)

V. Virsu and J. Rovamo, “Visual resolution, contrast sensitivity, and the cortical magnification factor,” Exp. Brain Res. 37(3), 475–494 (1979).
[Crossref] [PubMed]

Aghera, S.

S. Aghera, K. Krapels, J. Hixson, and R. G. Driggers, “Field verification of the Direct View Optics (DVO) Model for human facial identification,” Proc. SPIE 7343, 734311 (2009).
[Crossref]

Barten, P. G. J.

P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 52940 (2004).

Bui, T.

R. H. Vollmerhausen and T. Bui, “Using a targeting metric to predict the utility of an EO imager as a pilotage aid,” Proc. SPIE 6207, 62070C (2006).
[Crossref]

Driggers, R.

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43(11), 2806–2818 (2004).
[Crossref]

Driggers, R. G.

Hixson, J.

S. Aghera, K. Krapels, J. Hixson, and R. G. Driggers, “Field verification of the Direct View Optics (DVO) Model for human facial identification,” Proc. SPIE 7343, 734311 (2009).
[Crossref]

Hixson, J. G.

B. P. Teaney, D. M. Tomkinson, and J. G. Hixson, “Legacy modeling and range prediction comparison: NV-IPM versus SSCamIP and NVTherm,” Proc. SPIE 9452, 94520P (2015).
[Crossref]

R. H. Vollmerhausen, S. Moyer, K. Krapels, R. G. Driggers, J. G. Hixson, and A. L. Robinson, “Predicting the probability of facial identification using a specific object model,” Appl. Opt. 47(6), 751–759 (2008).
[Crossref] [PubMed]

Jacobs, E.

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43(11), 2806–2818 (2004).
[Crossref]

Krapels, K.

S. Aghera, K. Krapels, J. Hixson, and R. G. Driggers, “Field verification of the Direct View Optics (DVO) Model for human facial identification,” Proc. SPIE 7343, 734311 (2009).
[Crossref]

R. H. Vollmerhausen, S. Moyer, K. Krapels, R. G. Driggers, J. G. Hixson, and A. L. Robinson, “Predicting the probability of facial identification using a specific object model,” Appl. Opt. 47(6), 751–759 (2008).
[Crossref] [PubMed]

Moyer, S.

Robinson, A. L.

Rovamo, J.

V. Virsu and J. Rovamo, “Visual resolution, contrast sensitivity, and the cortical magnification factor,” Exp. Brain Res. 37(3), 475–494 (1979).
[Crossref] [PubMed]

Teaney, B. P.

B. P. Teaney, D. M. Tomkinson, and J. G. Hixson, “Legacy modeling and range prediction comparison: NV-IPM versus SSCamIP and NVTherm,” Proc. SPIE 9452, 94520P (2015).
[Crossref]

Tomkinson, D. M.

B. P. Teaney, D. M. Tomkinson, and J. G. Hixson, “Legacy modeling and range prediction comparison: NV-IPM versus SSCamIP and NVTherm,” Proc. SPIE 9452, 94520P (2015).
[Crossref]

Virsu, V.

V. Virsu and J. Rovamo, “Visual resolution, contrast sensitivity, and the cortical magnification factor,” Exp. Brain Res. 37(3), 475–494 (1979).
[Crossref] [PubMed]

Vollmerhausen, R.

Vollmerhausen, R. H.

R. H. Vollmerhausen, “Representing the observer in electro-optical target acquisition models,” Opt. Express 17, 017253 (2009).

R. H. Vollmerhausen, S. Moyer, K. Krapels, R. G. Driggers, J. G. Hixson, and A. L. Robinson, “Predicting the probability of facial identification using a specific object model,” Appl. Opt. 47(6), 751–759 (2008).
[Crossref] [PubMed]

R. H. Vollmerhausen, R. G. Driggers, and D. L. Wilson, “Predicting range performance of sampled imagers by treating aliased signal as target-dependent noise,” J. Opt. Soc. Am. A 25(8), 2055–2065 (2008).
[Crossref] [PubMed]

R. H. Vollmerhausen and T. Bui, “Using a targeting metric to predict the utility of an EO imager as a pilotage aid,” Proc. SPIE 6207, 62070C (2006).
[Crossref]

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43(11), 2806–2818 (2004).
[Crossref]

Wilson, D. L.

Appl. Opt. (2)

Exp. Brain Res. (1)

V. Virsu and J. Rovamo, “Visual resolution, contrast sensitivity, and the cortical magnification factor,” Exp. Brain Res. 37(3), 475–494 (1979).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (1)

Opt. Eng. (1)

R. H. Vollmerhausen, E. Jacobs, and R. Driggers, “New metric for predicting target acquisition performance,” Opt. Eng. 43(11), 2806–2818 (2004).
[Crossref]

Opt. Express (1)

R. H. Vollmerhausen, “Representing the observer in electro-optical target acquisition models,” Opt. Express 17, 017253 (2009).

Proc. SPIE (4)

R. H. Vollmerhausen and T. Bui, “Using a targeting metric to predict the utility of an EO imager as a pilotage aid,” Proc. SPIE 6207, 62070C (2006).
[Crossref]

B. P. Teaney, D. M. Tomkinson, and J. G. Hixson, “Legacy modeling and range prediction comparison: NV-IPM versus SSCamIP and NVTherm,” Proc. SPIE 9452, 94520P (2015).
[Crossref]

S. Aghera, K. Krapels, J. Hixson, and R. G. Driggers, “Field verification of the Direct View Optics (DVO) Model for human facial identification,” Proc. SPIE 7343, 734311 (2009).
[Crossref]

P. G. J. Barten, “Formula for the contrast sensitivity of the human eye,” Proc. SPIE 5294, 52940 (2004).

Other (6)

R. J. Beaton and W. W. Farley, “Comparative study of the MTFA, ICS, and SQRI image quality metrics for visual display systems,” Armstrong Lab., Air Force Systems Command, Wright-Patterson AFB, OH, Report AL-TR-1992–0001, DTIC ADA252116 (1991).

K. R. Boss and J. E. Lincoln, Engineering Data Compendium: Human Perception and Performance, Vol. 1, Harry G. Armstrong Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio (1988).

C. R. Carlson, “Sine-wave threshold contrast-sensitivity function: dependence on display size,” RCA Review,” 43 (1982).

R. H. Vollmerhausen, D. A. Reago, Jr., and R. G. Driggers, Analysis and Evaluation of Sampled Imaging Systems, Tutorial Texts in Optical Engineering Volume TT87, SPIE, Bellingham, WA (2010).

http://www.cerdec.army.mil/inside_cerdec/nvesd/integrated_performance_model/

R. H. Vollmerhausen, E. Jacobs, J. Hixson, and M. Friedman, “The Targeting Task Performance (TTP) Metric; A new model for predicting target acquisition performance,” Technical Report AMSEL-NV-TR-230, U.S. Army CERDEC, Fort Belvoir, VA 22060 (2006).

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Figures (13)

Fig. 1
Fig. 1 Test sets used to validate that the TTP metric accurately predicts the average PID.
Fig. 2
Fig. 2 The relative position of the rectangles determines letter identification.
Fig. 3
Fig. 3 The 144 image set consisted of the twelve aspects shown to right of each of the twelve tactical vehicles shown to left.
Fig. 4
Fig. 4 Data presented in NVIPM documentation in support of changing the way the TTP is calculated. In the graph to left, w equals 15 degrees; in the graph to right, w is set to the angle subtended by the target at range. All the data are from facial ID experiments.
Fig. 5
Fig. 5 Illustration of observer during CTF measurement.
Fig. 6
Fig. 6 The figure shows relative improvement in CSF as the number of sine wave cycles presented to the observer increases. The data from [13, 14] indicates that improvement depends upon the number of cycles and is independent of spatial frequencies. Further, there is no improvement beyond ten or eleven cycles. The lines are Barten numerical fits for 0.1, 0.5, 1, and 2 cycles per milliradian sine waves. Unlike the [13, 14] data, improvement depends upon spatial frequency and continues beyond 100 sine wave periods.
Fig. 7
Fig. 7 Facial identification data are compared to SOM predictions using Eqs. (1) and (2). The line is SOM model prediction and the various symbols are experimental data from seven experiments. Experiment labels are those used in the references.
Fig. 8
Fig. 8 Experimental data described in [1] plotted in three groups defined by angle subtended by the vehicle targets at the eye. The predictions use the original model.
Fig. 9
Fig. 9 The no-noise data from [1] compared to original and modified TTP calculations. The line represents perfect predictions, and the symbols show the data.
Fig. 10
Fig. 10 Experiment 25 data compared to the original TTP [1] and the modified TTP [8, 9].
Fig. 11
Fig. 11 Experiment 36 data compared to the original TTP and the modified TTP.
Fig. 12
Fig. 12 Predictions of the original and modified TTP models compared to Sensor 1 data.
Fig. 13
Fig. 13 Predictions of the original and modified TTP models compared to Sensor 2 data.

Tables (1)

Tables Icon

Table 1 Parameters for Thermal Cameras used in Field Test.

Equations (8)

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Φ= [ δ( C tgt (ξ,η, R ng ) CTF sys (ξ,η) ) ( C tgt (ξ,η, R ng ) CTF sys (ξ,η) ) dξdη R ng 2 ] 1/2 .
PID(Φ/Φ84)=erf(Φ/Φ84)= 2 π 0 Φ/Φ84 e t 2 dt
V50= L tgt Φ50
Φ50= Φ84 2.08
Φ= [ C TGT CT F sys (ξ) dξ R ng C TGT CT F sys (η) dη R ng ] 1/2
CTF( ξ )= [ aξ e bξ 1+0.06 e bξ ] 1
a= 540 ( 1+ 0.2 L ) 0.2 / ( 1+ 12 w 2 ( 1+5.8ξ ) 2 )
b=5.24 ( 1+ 29.2 L ) 0.15 .

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