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Uncertainty: Overview
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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图像分割的拓意识不确定性
Saumya Gupta1, Yikai Zhang2, Xiaoling Hu1,3
1Stony Brook University, NY, USA.
Advances in neural information processing systems
|November 1, 2024
概括
这项研究引入了一种新方法,用于估计诸如血管等复杂结构的细分不确定性. 它识别了易出错的专家审查领域,提高了注释准确性.
科学领域:
- 计算机视觉 计算机视觉
- 医学图像分析 医学图像分析
- 拓数据分析 拓数据分析
背景情况:
- 细分曲线结构 (血管,道路) 是困难的,因为信号较弱和复杂的拓.
- 半自动注释方法需要专家校对,需要高效的不确定性估计.
- 现有的像素智能不确定性地图对于拓结构验证是不够的.
研究的目的:
- 开发用于曲线结构细分的结构智能不确定性估计方法.
- 为了确定易出错的拓结构,进行有针对性的专家验证.
- 为了提高大规模注释任务的效率和准确性.
主要方法:
- 利用离散的摩尔斯理论 (DMT) 来捕获和分析拓结构.
- 提出了一个联合预测模型,用于考虑邻近元素的结构间不确定性估计.
- 引入了可能的DMT,用于结构内不确定性建模的扰动和行走方案.
主要成果:
- 与现有方法相比,提出的方法产生了优越的结构智能不确定性图.
- 在各种2D和3D数据集上证明了有效性.
- 成功识别了不确定的拓结构进行验证.
结论:
- 结构智能不确定性估计对于精确细分曲线结构至关重要.
- 使用DMT的新方法有效地模拟了内部和内部结构的不确定性.
- 这种方法有助于加快和提高专家注释的可靠性.


