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Related Concept Videos

Correlations02:20

Correlations

36.4K
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

Correlation and Causation

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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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Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
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Active Filters01:25

Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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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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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Latent Constrained Correlation Filter.

Baochang Zhang, Shangzhen Luan, Chen Chen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 11, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Latent constrained correlation filters (LCCF) improve object recognition by sampling solutions in a latent subspace. This method accounts for data variations, outperforming existing techniques in tasks like eye localization and car detection.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Correlation filters are effective for shift-invariant object recognition and robust to distortions.
    • Current methods combine sub-filters from limited data, akin to bagging, but neglect data variations.

    Purpose of the Study:

    • To introduce a novel method, latent constrained correlation filters (LCCF), to address limitations in existing correlation filter approaches.
    • To enhance object recognition by accounting for data variations through solution sampling.

    Main Methods:

    • Proposed Latent Constrained Correlation Filters (LCCF) by mapping filters to a latent subspace.
    • Developed a new learning framework embedding distribution-related constraints.
    • Introduced a subspace-based alternating direction method of multipliers for optimization, proving convergence.

    Main Results:

    • LCCF successfully applied to eye localization, car detection, and object tracking.
    • Demonstrated superior performance compared to state-of-the-art methods in extensive experiments.

    Conclusions:

    • The proposed LCCF method effectively handles data variations by incorporating solution sampling.
    • LCCF offers a robust and high-performing solution for various computer vision tasks.