What is GLCM algorithm?

What is GLCM algorithm?

A co-occurrence matrix measures the probability of appearance of pairs of pixel values located at a distance in the image. This algorithm is known as GLCM. The matrix defines the probability of joining two pixels , ( , ) that have values i and j with distance d and as an orientation angular.

What is GLCM used for?

The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix.

How many features are there in GLCM?

This paper presents an application of gray level co-occurrence matrix (GLCM) to extract second order statistical texture features for motion estimation of images. The Four features namely, Angular Second Moment, Correlation, Inverse Difference Moment, and Entropy are computed using Xilinx FPGA.

What is energy in GLCM?

‘Energy’ Returns the sum of squared elements in the GLCM. Range = [0 1] Energy is 1 for a constant image. The property Energy is also known as uniformity, uniformity of energy, and angular second moment.

Is GLCM machine learning?

GLCM Based Feature Extraction and Medical X-RAY Image Classification using Machine Learning Techniques. Abstract: The machine learning and artificial intelligence play a vital role to solve the challenging issues in Clinical imaging.

What is dissimilarity in GLCM?

(f) Dissimilarity is a measure of distance between pairs of objects (pixels) in the region of interest.

What is GLCM homogeneity?

‘Homogeneity’ Returns a value that measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal. Range = [0 1] Homogeneity is 1 for a diagonal GLCM.

What is GLCM entropy?

A gray level co-occurence matrix (GLCM) is a histogram of co-occurring grayscale values at a given offset over an image. To describe the texture of an image it is usual to extract features such as entropy, energy, contrast, correlation, etc. from several co-occurrence matrices computed for different offsets.