Efficiency of human and model observers for signal-detection tasks in non-Gaussian distributed lumpy backgrounds

Subok Park, Eric Clarkson, Matthew A. Kupinski, Harrison H. Barrett

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Efficiencies of the human observer and channelized-Hotelling observers (CHOs) relative to the ideal observer for signal-detection tasks are discussed. A CHO using Laguerre-Gauss channels, which we call an efficient CHO (eCHO), and a CHO adding a scanning scheme to the eCHO to include signal-location uncertainty, which we call a scanning eCHO (seCHO), are considered. 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 the tasks. 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. A maximum-likelihood estimation method is employed to fit smooth psychometric curves with observer performance measurements. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to a standard observer. Depending on image statistics, the ideal observer or the Hotelling observer is used as the standard observer. The results show that the eCHO performs poorly in detecting signals with location uncertainty and the seCHO performs only slightly better while the ideal observer outperforms the human observer and CHOs for both the tasks. Human efficiencies are approximately less than 2.5% and 41%, respectively, for the SKE and SKS tasks, where the gray levels of the lumpy background are non-Gaussian distributed. These results also imply that human observers are not affected by signal-location uncertainty as much as the ideal observer. However, for the SKE tasks using Gaussian-distributed lumpy backgrounds, the human efficiency ranges between 28% and 42%. 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 tasks.

Original languageEnglish (US)
Article number17
Pages (from-to)138-149
Number of pages12
JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
StatePublished - 2005
EventMedical Imaging 2005 - Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States
Duration: Feb 15 2005Feb 17 2005


  • Bayesian ideal observer
  • Hotelling observers
  • Human efficiency
  • Non-Gaussian distributed lumpy backgrounds

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Efficiency of human and model observers for signal-detection tasks in non-Gaussian distributed lumpy backgrounds'. Together they form a unique fingerprint.

Cite this