Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Calibration Curves: Correlation Coefficient

Calibration Curves: Correlation Coefficient

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 other increases, and...

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

Coefficient of Correlation

Coefficient of Correlation

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 strength of the linear...

Calculating and Interpreting the Linear Correlation Coefficient

Calculating and Interpreting the Linear Correlation Coefficient

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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:

Wilcoxon Signed-Ranks Test for Matched Pairs

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance

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 or...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Groupwise Image Alignment via Self Quotient Images.

Sensors (Basel, Switzerland)·2020

Same author

A Kronecker Product Model for Repeated Pattern Detection on 2D Urban Images.

IEEE transactions on pattern analysis and machine intelligence·2018

Same author

Fusion of range and stereo data for high-resolution scene-modeling.

IEEE transactions on pattern analysis and machine intelligence·2015

Same author

Efficient subframe video alignment using short descriptors.

IEEE transactions on pattern analysis and machine intelligence·2013

Search research articles

Related Experiment Video

Updated: Jul 2, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Parametric image alignment using enhanced correlation coefficient maximization.

Georgios D Evangelidis¹, Emmanouil Z Psarakis

¹Signal Processing and Communications Lab, Department of Computer Engineering and Informatics, University of Patras, Rio-Patras, Greece. evagelid@ceid.upatras.gr

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 16, 2008

Summary

This summary is machine-generated.

This study introduces a modified correlation coefficient for robust image alignment, invariant to photometric distortions. The new method offers accurate alignments and faster convergence, even under noisy conditions.

Related Experiment Videos

Last Updated: Jul 2, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

Computer Vision
Image Processing
Computational Photography

Background:

Image alignment is crucial for various applications.
Existing methods struggle with photometric distortions and noise.
Need for robust and efficient image alignment techniques.

Purpose of the Study:

To propose a modified correlation coefficient for image alignment.
To develop iterative optimization schemes for maximizing the new similarity measure.
To evaluate the performance against established algorithms.

Main Methods:

A modified correlation coefficient invariant to photometric distortions.
Two iterative maximization schemes: forward additive and inverse compositional.
Approximation of the objective function for closed-form solutions.

Main Results:

The forward version achieves more accurate alignments and faster convergence than Lucas-Kanade.
The inverse version shows comparable performance to the Simultaneous Inverse Compositional (SIC) algorithm.
The inverse version offers lower computational complexity than SIC.

Conclusions:

The proposed modified correlation coefficient is effective for image alignment.
The iterative schemes provide efficient and accurate solutions.
The method demonstrates robustness under noisy conditions and photometric distortions.