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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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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Interpretation of Confidence Intervals01:19

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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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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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
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Learning and Selecting Confidence Measures for Robust Stereo Matching.

Min-Gyu Park, Kuk-Jin Yoon

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    We developed a robust stereo matching method using supervised learning for confidence prediction. This approach enhances accuracy and robustness in challenging environments by selecting confidence measures and modulating matching costs.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Stereo Vision

    Background:

    • Stereo matching algorithms are crucial for 3D reconstruction.
    • Existing methods struggle with robustness in uncontrolled environments.
    • Accurate disparity map computation is essential for various applications.

    Purpose of the Study:

    • To enhance the robustness and accuracy of disparity map computation.
    • To introduce a supervised learning-based confidence prediction for stereo matching.
    • To improve (semi-)global stereo matching algorithms using confidence-based cost modulation.

    Main Methods:

    • Analyzed confidence measures within the random forest framework.
    • Selected effective confidence measures based on data and matching strategies.
    • Trained a random forest for efficient confidence prediction.
    • Developed a confidence-based matching cost modulation scheme.
    • Applied the scheme to popular stereo matching algorithms.

    Main Results:

    • Achieved improved efficiency in confidence prediction.
    • Enhanced the robustness and accuracy of stereo matching algorithms.
    • Demonstrated effectiveness on challenging outdoor datasets (KITTI, Middlebury).

    Conclusions:

    • The proposed confidence measure selection and cost modulation schemes significantly improve stereo matching.
    • The approach provides a robust solution for disparity map computation in real-world scenarios.
    • Validated effectiveness through extensive experimental verification on benchmark datasets.