Model mismatch and the ideal observer in SPECT

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

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Image quality, the ideal observer, and human performance of radiologic decision tasks

Academic Radiology ◽

10.1016/s1076-6332(05)80411-8 ◽

1995 ◽

Vol 2 (6) ◽

pp. 522-526 ◽

Cited By ~ 27

Author(s):

Arthur Burgess

Keyword(s):

Image Quality ◽

Human Performance ◽

Ideal Observer ◽

The Ideal ◽

Decision Tasks

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Tactile orientation perception: an ideal observer analysis of human psychophysical performance in relation to macaque area 3b receptive fields

Journal of Neurophysiology ◽

10.1152/jn.00631.2015 ◽

2015 ◽

Vol 114 (6) ◽

pp. 3076-3096 ◽

Cited By ~ 9

Author(s):

Ryan M. Peters ◽

Phillip Staibano ◽

Daniel Goldreich

Keyword(s):

Cortical Neurons ◽

Human Performance ◽

Spatial Perception ◽

Receptive Fields ◽

Human Perception ◽

Ideal Observer ◽

Orientation Discrimination ◽

Orientation Perception ◽

Area 3B ◽

The Ideal

The ability to resolve the orientation of edges is crucial to daily tactile and sensorimotor function, yet the means by which edge perception occurs is not well understood. Primate cortical area 3b neurons have diverse receptive field (RF) spatial structures that may participate in edge orientation perception. We evaluated five candidate RF models for macaque area 3b neurons, previously recorded while an oriented bar contacted the monkey's fingertip. We used a Bayesian classifier to assign each neuron a best-fit RF structure. We generated predictions for human performance by implementing an ideal observer that optimally decoded stimulus-evoked spike counts in the model neurons. The ideal observer predicted a saturating reduction in bar orientation discrimination threshold with increasing bar length. We tested 24 humans on an automated, precision-controlled bar orientation discrimination task and observed performance consistent with that predicted. We next queried the ideal observer to discover the RF structure and number of cortical neurons that best matched each participant's performance. Human perception was matched with a median of 24 model neurons firing throughout a 1-s period. The 10 lowest-performing participants were fit with RFs lacking inhibitory sidebands, whereas 12 of the 14 higher-performing participants were fit with RFs containing inhibitory sidebands. Participants whose discrimination improved as bar length increased to 10 mm were fit with longer RFs; those who performed well on the 2-mm bar, with narrower RFs. These results suggest plausible RF features and computational strategies underlying tactile spatial perception and may have implications for perceptual learning.

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