Likelihood ratio for a binary Bayesian classifier under a noise-exclusion model
This provides a novel framework for optimizing imaging systems and algorithms in medical perception and other domains, though it appears incremental as an extension of ideal observer models.
The paper tackles the problem of holistic visual search processing by developing a new statistical ideal observer model that reduces free parameters through thresholds on minimum extractable image features. The result is a framework applicable to medical image perception, computer vision, and defense/security for optimizing systems and evaluating performance.
We develop a new statistical ideal observer model that performs holistic visual search (or gist) processing in part by placing thresholds on minimum extractable image features. In this model, the ideal observer reduces the number of free parameters thereby shrinking down the system. The applications of this novel framework is in medical image perception (for optimizing imaging systems and algorithms), computer vision, benchmarking performance and enabling feature selection/evaluations. Other applications are in target detection and recognition in defense/security as well as evaluating sensors and detectors.