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Related Concept Videos

Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
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Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
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Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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Calibration Curves: Correlation Coefficient01:10

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Correlations02:20

Correlations

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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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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Fringe-adjusted joint transform correlation.

M S Alam, M A Karim

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    |September 11, 2010
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    Summary
    This summary is machine-generated.

    A fringe-adjusted joint transform correlator (JTC) significantly improves correlation discrimination for single and multiple objects. This advanced JTC avoids complex processing, offering superior performance over classical and binary JTCs.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Pattern Recognition

    Background:

    • Joint Transform Correlators (JTCs) are widely used for pattern recognition.
    • Classical and binary JTCs face limitations in correlation discrimination, especially with complex scenes.
    • Computational complexity in binarization steps can hinder real-time applications.

    Purpose of the Study:

    • To introduce and evaluate a fringe-adjusted joint transform correlator (JTC) for enhanced correlation discrimination.
    • To compare the performance of the fringe-adjusted JTC against classical and binary JTCs.
    • To propose optical implementations for the fringe-adjusted JTC.

    Main Methods:

    • Utilizing a fringe-adjusted joint power spectrum in the JTC architecture.
    • Analyzing correlation output for input scenes with single and multiple objects.
    • Comparing performance metrics with classical and binary JTC techniques.

    Main Results:

    • The fringe-adjusted JTC demonstrates significantly improved correlation discrimination.
    • Superior correlation output is observed for both single and multiple object scenarios.
    • The proposed method eliminates the need for computation-intensive Fourier-plane binarization.

    Conclusions:

    • The fringe-adjusted JTC offers a more effective approach to correlation discrimination.
    • This technique provides a practical advantage by simplifying the processing pipeline.
    • Further optical implementations are suggested for practical realization.