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.

翻译：我们提出了一种新的统计理想观测器模型，该模型通过设定最小可提取图像特征的阈值，部分实现了整体视觉搜索（或要旨）处理。在此模型中，理想观测器减少了自由参数的数量，从而压缩了系统规模。这一新颖框架可应用于医学图像感知（用于优化成像系统与算法）、计算机视觉、性能基准测试以及特征选择/评估。其他应用领域包括国防/安全中的目标检测与识别，以及传感器与探测器的评估。