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Models for predicting perception of an item of interest

机译:预测感兴趣项目感知的模型

摘要

Statistical models for quantifying and predicting human perception of scratches on automotive components are disclosed. Such models may be created by utilizing quantitative two-step moving scale surveys. Such surveys employ a continuous scale to model human perception and allow response bias and measurement error in survey data to be evaluated. Once survey data is collected, relationships between the visual perception of the scratches and the measurable optical properties associated with the scratches can be determined. Additionally, relationships between the visual perception of the scratches and the actual physical scratch dimensions can be determined. Thereafter, models for predicting the human perception of such scratches can be created therefrom. Since these models predict the results of such surveys, the need for repeatedly collecting survey data is eliminated. These models may also be used for predicting human perception of other items of interest. Furthermore, the two-step moving scale surveys may be used in various kinds of surveys about any items of interest.
机译:公开了用于量化和预测人类对汽车部件上的划痕的感知的统计模型。可以通过利用定量的两步移动规模调查来创建此类模型。这样的调查采用连续的比例来模拟人类的感知,并允许评估调查数据中的响应偏差和测量误差。一旦收集了调查数据,就可以确定划痕的视觉感知与与划痕相关的可测量的光学性能之间的关系。另外,可以确定划痕的视觉感知与实际的物理划痕尺寸之间的关系。此后,可以由此创建用于预测人类对这种划痕的感知的模型。由于这些模型可以预测此类调查的结果,因此无需重复收集调查数据。这些模型还可用于预测人类对其他感兴趣项目的感知。此外,两步移动比例调查可以用于关于任何感兴趣项目的各种调查中。

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