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Related Concept Videos

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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Correlation and Causation01:27

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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.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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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.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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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.
The size of the correlation r indicates the...
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Dynamic Equilibrium02:20

Dynamic Equilibrium

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A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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Related Experiment Video

Updated: Jan 20, 2026

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
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Dynamically Spatiotemporal Regularized Correlation Tracking.

Yuhui Zheng, Huihui Song, Kaihua Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 24, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a dynamic spatiotemporal regularization model for correlation filter (CF)-based visual tracking. It improves tracking accuracy by adapting regularization weights, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Spatially regularized strategies are used in correlation filter (CF)-based visual tracking to mitigate boundary effects.
    • Existing methods use fixed Gaussian functions for regularization, which can lead to model degradation due to inflexible constraints on changing CFs.

    Purpose of the Study:

    • To develop a dynamically spatiotemporal regularization model for improved visual tracking.
    • To address the limitations of fixed regularization weights in CF-based trackers.

    Main Methods:

    • A novel dynamically spatiotemporal regularization model is proposed.
    • The model jointly learns CFs and adaptive regularization weights from consecutive frames.
    • Efficient solution in the Fourier domain using the alternative direction method.

    Main Results:

    • The proposed tracker demonstrates favorable performance against baseline and state-of-the-art methods.
    • Evaluations conducted on OTB-100 and VOT-2016 datasets.

    Conclusions:

    • The dynamically spatiotemporal regularization model effectively enhances visual tracking accuracy.
    • Adaptive regularization weights provide more flexible constraints for evolving CFs.