Active mutual conjoint estimation of multiple contrast sensitivity functions.

IF 2 4区 心理学 Q2 OPHTHALMOLOGY
Dom C P Marticorena, Quinn Wai Wong, Jake Browning, Ken Wilbur, Pinakin Gunvant Davey, Aaron R Seitz, Jacob R Gardner, Dennis L Barbour
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引用次数: 0

Abstract

Recent advances in nonparametric contrast sensitivity function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine learning CSF estimation with Gaussian processes allows for design optimization in the kernel, acquisition function, and underlying task representation, to name a few. This article describes a novel kernel for CSF estimation that is more flexible than a kernel based on strictly functional forms. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities, or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.

多种对比敏感度函数的主动相互联合估计。
非参数对比灵敏度函数(CSF)估算的最新进展在准确性和效率之间实现了新的权衡,这是传统参数估算方法所不具备的。这种新框架的另一个优势是能够独立调整估计器的多个方面,以寻求进一步的改进。利用高斯过程的机器学习 CSF 估计可以在内核、获取函数和底层任务表示等方面进行设计优化。本文介绍了一种用于 CSF 估计的新型核,它比基于严格函数形式的核更加灵活。尽管更灵活,但它能产生更高效的估计器。此外,超越纯信息增益的数据采集试验选择也能提高估算质量。最后,在一般 CSF 形状的基础上引入潜变量表征,可以同时估算多个 CSF,例如来自不同眼睛、偏心率或亮度的 CSF。本文介绍了新程序比以前的非参数估计程序表现更好的条件,并对其进行了量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Vision
Journal of Vision 医学-眼科学
CiteScore
2.90
自引率
5.60%
发文量
218
审稿时长
3-6 weeks
期刊介绍: Exploring all aspects of biological visual function, including spatial vision, perception, low vision, color vision and more, spanning the fields of neuroscience, psychology and psychophysics.
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