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Related Concept Videos

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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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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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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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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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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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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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Disparity-selective stereo matching using correlation confidence measure.

Sijung Kim, Jinbeum Jang, Jaeseung Lim

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |September 6, 2018
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    Summary
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    This study introduces a robust stereo matching method that improves accuracy in occluded regions by combining CENSUS and SIFT transforms. The new approach enhances disparity estimation for challenging areas in stereo vision.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Cost-volume filtering (CVF) methods offer fast and accurate stereo matching.
    • CVF struggles with occluded and texture-free regions, leading to incorrect disparity results.
    • Pixel-unit cost aggregation in CVF is computationally intensive due to image resolution and search range dependencies.

    Purpose of the Study:

    • To develop a robust stereo matching method specifically for occluded regions.
    • To improve the accuracy and efficiency of disparity estimation in challenging visual scenes.
    • To address the limitations of existing CVF methods in handling occlusions.

    Main Methods:

    • Generated cost volumes using CENSUS transform and scale-invariant feature transform (SIFT).
    • Aggregated label-based cost volumes using adaptive support weight and simple linear iterative clustering (SLIC).
    • Selected optimal disparity by identifying high-confidence similarities between CENSUS/SIFT and minimum cost points.

    Main Results:

    • The proposed method successfully estimates optimal disparity in occluded regions.
    • Demonstrated improved performance in handling scenes with occlusion information present in only one stereo pair.
    • Experimental results validate the effectiveness of the combined CENSUS and SIFT approach with SLIC aggregation.

    Conclusions:

    • The novel stereo matching technique effectively resolves disparities in occluded areas.
    • The integration of CENSUS, SIFT, and SLIC provides a more robust solution for stereo matching challenges.
    • This method offers a significant advancement for applications requiring accurate stereo vision, particularly in the presence of occlusions.