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相关概念视频

Uncertainty: Overview00:59

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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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

732
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...
732
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

557
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
557
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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相关实验视频

Updated: Jul 24, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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单眼3D物体检测的不确定性预测

Junghwan Mun1, Hyukdoo Choi1

  • 1Department of Electronic Materials, Devices, and Equipment Engineering, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

估计对象检测的不确定性对于自动驾驶汽车至关重要. 本研究引入了一种不确定性模型,通过结合阻塞信息来提高准确性,提高路径规划安全性.

关键词:
深度学习是一种深度学习.对象检测检测对象检测对象检测自动驾驶自动驾驶的自动驾驶不确定性估计估计的不确定性不确定性评估不确定性评估

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 准确的物体检测对于自主系统至关重要,但量化检测不确定性仍然是一个挑战.
  • 现有的研究优先考虑检测准确度而不是不确定性估计,阻碍了自动驾驶汽车的安全导航.

研究的目的:

  • 开发和验证单眼3D物体检测的不确定性模型.
  • 通过可靠的不确定性估计来提高自动驾驶汽车的安全性.

主要方法:

  • 开发了一种多层感知子 (MLP) 不确定性模型,以预测界限框参数的标准偏差.
  • 一个新的单眼检测模型被设计为与检测一起分类对象封闭水平.
  • 输入特征包括边界框参数,类概率和封闭概率.

主要成果:

  • 不确定性模型成功地预测了检测到的对象的不确定性.
  • 结合封闭信息,平均不确定性误差降低了7.1%.
  • 该模型直接估计了绝对尺度上的总不确定性,在KITTI基准上进行了验证.

结论:

  • 提出的不确定性模型有效量化了单眼3D物体检测中的检测不确定性.
  • 封闭信息显著提高了不确定性预测的准确性.
  • 这种方法为自动驾驶系统的安全路径规划提供了关键不确定性估计.