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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 22, Iss. 1 — Jan. 1, 2005
  • pp: 3–16

Efficiency of the human observer detecting random signals in random backgrounds

Subok Park, Eric Clarkson, Matthew A. Kupinski, and Harrison H. Barrett  »View Author Affiliations


JOSA A, Vol. 22, Issue 1, pp. 3-16 (2005)
http://dx.doi.org/10.1364/JOSAA.22.000003


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Abstract

The efficiencies of the human observer and the channelized-Hotelling observer relative to the ideal observer for signal-detection tasks are discussed. Both signal-known-exactly (SKE) tasks and signal-known-statistically (SKS) tasks are considered. Signal location is uncertain for the SKS tasks, and lumpy backgrounds are used for background uncertainty in both cases. Markov chain Monte Carlo methods are employed to determine ideal-observer performance on the detection tasks. Psychophysical studies are conducted to compute human-observer performance on the same tasks. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to the ideal observer. Human efficiencies are approximately 2.1% and 24%, respectively, for the SKE and SKS tasks. The results imply that human observers are not affected as much as the ideal observer by signal-location uncertainty even though the ideal observer outperforms the human observer for both tasks. Three different simplified pinhole imaging systems are simulated, and the humans and the model observers rank the systems in the same order for both the SKE and the SKS tasks.

© 2005 Optical Society of America

OCIS Codes
(110.3000) Imaging systems : Image quality assessment
(330.5510) Vision, color, and visual optics : Psychophysics
(330.6100) Vision, color, and visual optics : Spatial discrimination

History
Original Manuscript: February 2, 2004
Revised Manuscript: July 30, 2004
Published: January 1, 2005

Citation
Subok Park, Eric Clarkson, Matthew A. Kupinski, and Harrison H. Barrett, "Efficiency of the human observer detecting random signals in random backgrounds," J. Opt. Soc. Am. A 22, 3-16 (2005)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-22-1-3


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References

  1. R. H. S. Carpenter, J. G. Robson, eds., Vision Research: A Practical Guide to Laboratory Methods (Oxford U. Press, New York, 1998).
  2. B. D. Gallas, H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725–1738 (2003). [CrossRef]
  3. M. A. Kupinski, J. W. Hoppin, E. Clarkson, H. H. Barrett, “Ideal observer computation using Markov-chain Monte Carlo,” J. Opt. Soc. Am. A 20, 430–438 (2003). [CrossRef]
  4. S. Park, M. A. Kupinski, E. Clarkson, H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, Vol. 2732 of Lecture Notes in Computer Science, C. J. Taylor, J. A. Noble, eds. (Springer-Verlag, New York, 2003), pp. 342–353.
  5. J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992). [CrossRef] [PubMed]
  6. H. H. Barrett, C. Abbey, B. Gallas, M. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in Medical Imaging 1998: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998). [CrossRef]
  7. H. H. Barrett, K. J. Myers, Foundations of Image Science (Wiley, New York, 2004).
  8. D. G. Pelli, “Uncertainty explains many aspects of visual contrast detection and discrimination,” J. Opt. Soc. Am. A 2, 1508–1532 (1985). [CrossRef] [PubMed]
  9. R. G. Swensson, P. F. Judy, “Detection of noisy visual targets: Models for the effects of spatial uncertainty and signal-to-noise ratio,” Percept. Psychophys. 29, 521–534 (1981). [CrossRef] [PubMed]
  10. R. M. Manjeshwar, D. L. Wilson, “Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences,” J. Opt. Soc. Am. A 18, 78–85 (2001). [CrossRef]
  11. W. P. Tanner, T. G. Birdsall, “Definitions of d′ and η as psychophysical measures,” J. Acoust. Soc. Am. 30, 922–928 (1958). [CrossRef]
  12. A. E. Burgess, R. F. Wagner, R. J. J. Jennings, “Statistical efficiency: a measure of human visual signal detection performance,” J. Appl. Photogr. Eng. 8, 76–78 (1982).
  13. A. E. Burgess, H. Chandharian, “Visual signal detection. II. Signal-location identification,” J. Opt. Soc. Am. A 1, 906–910 (1984). [CrossRef] [PubMed]
  14. A. E. Burgess, Xing Li, C. K. Abbey, “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997). [CrossRef]
  15. A. E. Burgess, F. L. Jacobson, P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001). [CrossRef] [PubMed]
  16. F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Search for lesions in mammograms: statistical characterization of observer responses,” Med. Phys. 31, 24–36 (2004). [CrossRef] [PubMed]
  17. H. H. Barrett, C. K. Abbey, E. Clarkson, “Objective assessment of image quality III: ROC metrics, ideal observers, and likelihood-generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998). [CrossRef]
  18. H. C. Gifford, P. H. Pretorius, M. A. King, “Comparison of human- and model-observer LROC studies,” in Medical Imaging 2003: Image Perception, D. P. Chakraborty, E. A. Krupinski, eds., Proc. SPIE5034, 112–122 (2003). [CrossRef]
  19. F. A. Wichmann, N. J. Hill, “The psychometric function: I. Fitting, sampling, and goodness of fit,” Percept. Psychophys. 63, 1314–1329 (2001). [CrossRef]
  20. F. A. Wichmann, N. J. Hill, “The psychometric function: II. Bootstrap-based confidence intervals and sampling,” Percept. Psychophys. 63, 1314–1329 (2001). [CrossRef]
  21. J. Nachmias, “On the psychometric function for contrast detection,” Vision Res. 21, 215–223 (1981). [CrossRef] [PubMed]
  22. R. F. Quick, “A vector magnitude model of contrast detection,” Kybernetik 16, 65–67 (1974). [CrossRef]
  23. W. Weibull, “Statistical distribution function of wide applicability,” J. Appl. Mech. 18, 292–297 (1951).
  24. C. E. Metz, B. A. Herman, J. H. Shen, “Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data,” Stat. Med. 17, 1033–1053 (1998). [CrossRef] [PubMed]
  25. C. E. Metz, X. Pan, “Proper binormal ROC curves: theory and maximum-likelihood estimation,” J. Math. Psychol. 43, 1–33 (1999). [CrossRef] [PubMed]
  26. P. F. Judy, M. F. Kijewski, R. G. Swensson, “Observer detection performance loss: target size uncertainty,” in Medical Imaging 1997: Image Perception, E. A. Krupinski, D. P. Chakraborty, eds., Proc. SPIE3036, 39–47 (1997). [CrossRef]
  27. M. P. Eckstein, C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” in Medical Imaging 2001: Image Perception, E. A. Krupinski, D. P. Chakraborty, eds., Proc. SPIE4324, 91–102 (2001). [CrossRef]
  28. Y. Zhang, B. T. Pham, M. P. Eckstein, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in X-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004). [CrossRef] [PubMed]

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