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

Shape and Texture of Coarse Aggregate

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...

Correlation and Regression

Correlation and Regression

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 negative...

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...

IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

Correlation

Correlation

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:

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...

You might also read

Related Articles

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

Sort by

Same author

Recent results of medium wave infrared compressive sensing.

Applied optics·2015

Same author

Distance-classifier correlation filters for multiclass target recognition.

Applied optics·2010

Same author

Statistical inference for automatic target recognition systems.

Applied optics·2010

Same author

Unconstrained correlation filters.

Applied optics·2010

Same author

Theoretical framework for the design of purely real synthetic-discriminant-function-type correlation filters.

Applied optics·2010

Same author

Performance evaluation of minimum average correlation energy filters.

Applied optics·2010

Search research articles

Related Experiment Videos

Application of correlation filters for texture recognition.

A Mahalanobis, H Singh

|October 2, 2010

Summary

We developed a new statistical method for designing spatial filters that effectively discriminate between various textures, enabling fast, real-time texture recognition without segmentation using optical correlators.

Area of Science:

Image processing
Statistical modeling
Pattern recognition

Background:

Traditional correlation filters focus on shape recognition, limiting their use for texture discrimination.
Real-time texture analysis often requires segmentation and complex on-line computations.
Existing methods struggle to differentiate between similar textural patterns efficiently.

Purpose of the Study:

To introduce a novel statistical method for designing spatial filters specifically for texture discrimination.
To enable real-time texture recognition without the need for image segmentation.
To develop filters that can differentiate between various textures like terrains and random fields.

Main Methods:

The proposed method models textures as stationary random processes within image regions.

Related Experiment Videos

Optimum spatial filter coefficients are determined using eigenvector analysis.

The filters are designed to avoid on-line statistical computations for texture information extraction.

Main Results:

The developed spatial filters effectively discriminate between different textures.
The method allows for fast, real-time texture recognition using optical or digital correlators.
No image segmentation is required, simplifying the recognition process.

Conclusions:

The proposed statistical method offers an efficient approach for texture discrimination.
The designed spatial filters are suitable for real-time applications in image analysis.
This technique advances texture recognition capabilities by leveraging eigenvector analysis.