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Uncertainty: Overview

Uncertainty: Overview

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.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

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...

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...

Propagation of Uncertainty from Systematic Error

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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...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

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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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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Uncertainty Prediction for Monocular 3D Object Detection.

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

Summary

This summary is machine-generated.

Estimating uncertainty in object detection is crucial for self-driving cars. This study introduces an uncertainty model that improves accuracy by incorporating occlusion information, enhancing path planning safety.

Keywords:

deep learning object detection self-driving uncertainty estimation uncertainty evaluation

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Accurate object detection is vital for autonomous systems, but quantifying detection uncertainty remains a challenge.
Existing research prioritizes detection accuracy over uncertainty estimation, hindering safe navigation in self-driving vehicles.

Purpose of the Study:

To develop and validate an uncertainty model for monocular 3D object detection.
To improve the safety of self-driving vehicles by enabling reliable uncertainty estimation.

Main Methods:

A multi-layer perceptron (MLP) uncertainty model was developed to predict the standard deviation of bounding box parameters.
A novel monocular detection model was designed to classify object occlusion levels alongside detection.
Input features included bounding box parameters, class probabilities, and occlusion probabilities.

Main Results:

The uncertainty model successfully predicted uncertainties for detected objects.
Incorporating occlusion information reduced the mean uncertainty error by 7.1%.
The model directly estimated total uncertainty at an absolute scale, validated on the KITTI benchmark.

Conclusions:

The proposed uncertainty model effectively quantifies detection uncertainty in monocular 3D object detection.
Occlusion information significantly enhances the accuracy of uncertainty prediction.
This approach provides critical uncertainty estimates for safe path planning in autonomous driving systems.