提出噪声剔除模型下的二元贝叶斯分类器似然比新方法。
Likelihood ratio for a binary Bayesian classifier under a noise-exclusion model
- 基于最小可提取特征阈值的全局视觉搜索建模。
- 减少自由参数，简化系统复杂度。
- 适用于医学影像、安防检测与传感器评估。
我们提出一种新的统计理想观察者模型，该模型通过在可提取图像特征上设置阈值，部分实现整体视觉搜索（或图像概貌）处理。在此模型中，理想观察者通过减少自由参数来降低系统复杂度。该新框架的应用包括医学图像感知（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与评估。其他应用领域还包括国防与安全中的目标检测与识别，以及传感器与探测器的性能评估。
原文摘要 · Abstract (English)
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.
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