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Related Concept Videos

Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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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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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.  Highly accurate...
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Accuracy, limits, and approximation01:28

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Uncertainty: Confidence Intervals00:54

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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...
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Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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A Memory- and Accuracy-Aware Gaussian Parameter-Based Stereo Matching Using Confidence Measure.

Yeongmin Lee, Chong-Min Kyung

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    |December 24, 2019
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    This summary is machine-generated.

    This study introduces a memory-efficient stereo matching algorithm for resource-limited systems. It significantly reduces memory usage while maintaining robust depth map accuracy, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • Accurate stereo matching demands high memory and bandwidth, limiting its application in mobile devices.
    • Increasing pixel resolution and disparity levels exacerbate these resource constraints.

    Purpose of the Study:

    • To develop a memory-efficient and robust stereo matching algorithm.
    • To address the limitations of current stereo matching techniques in resource-constrained environments.

    Main Methods:

    • Employed a semiglobal parametric approach for cost aggregation using Gaussian mixture models.
    • Optimized memory usage by storing only significant Gaussian parameters during forward scanning.
    • Integrated a learning-based confidence measure using a random forest framework.

    Main Results:

    • Achieved over 97% reduction in memory requirements compared to standard semiglobal matching (SGM).
    • Demonstrated robust depth map generation on the KITTI dataset.
    • Outperformed state-of-the-art SGM-based algorithms in terms of efficiency and accuracy.

    Conclusions:

    • The proposed algorithm offers a significant advancement in memory efficiency for stereo matching.
    • Enables high-quality stereo matching in memory-limited applications like mobile robotics.
    • Provides a robust and computationally feasible solution for real-time depth perception.