cooccurrence matrix

cooccurrence matrix

(mathematics)
Given a position operator P(i,j), let A be a nxn matrix whose element A[i][j] is the number of times that points with grey level (intensity) g[i] occur, in the position specified by P, relative to points with grey level g[j]. Let C be the nxn matrix that is produced by dividing A with the total number of point pairs that satisfy P. C[i][j] is a measure of the joint probability that a pair of points satisfying P will have values g[i], g[j]. C is called a cooccurrence matrix defined by P. Examples for the operator P are: "i above j", "i one position to the right and two below j", etc.
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TABLE 5: Normalized cooccurrence matrix. (a) For cipher text obtained from the Latin text 0.3722 0.2212 0.0623 0.0048 0.2220 0.0343 0.0072 0 0.0623 0.0072 0.0016 0 0.0048 0 0 0 (b) For cipher text obtained from Cyrillic text 0.5863 0.0327 0.1326 0.0064 0.0391 0.0104 0.0144 0 0.1262 0.0200 0.0224 0.0016 0.0072 0 0.0008 0 TABLE 6: Cooccurrence descriptiors for Latin and Cyrillic cipher text.
introduced the textural features extracted by local binary pattern (LBP) and cooccurrence matrix [1].
In this section, we present the main details of word-to-word cooccurrence matrix construction, coding coefficient solving, and codebook learning due to their significant influence on the proposed model.
Grey-Level Cooccurrence Matrix (GLCM) algorithm, based on the second-order combination of the conditional probability density of the image, is more efficient in characterizing textures [9].
The gray level cooccurrence matrix (GLCM) is recognized as the most representative algorithm in spatial texture-related research.
Cooccurrence matrix extraction algorithm is a kind of statistical method of texture feature which is recognized by people, and it is a relatively mature method.
The proposed solution uses the structural cooccurrence matrix to calculate how close the handwritten trace of the patient is to the exam template and is combined with Naive Bayes, OPF, and SVM classifiers, showing to be a promising tool for the diagnosis of Parkinson's disease.
Texture features were also derived from the Haralick texture features based on the gray-level cooccurrence matrix (GLCM) or the gray-level difference vector.
where [d.sub.ij] represents the ith row and jth column element of the risk level cooccurrence matrix (both i and j represent the instantaneous driving risk level index).
(1) Gray Level Cooccurrence Matrix. Gray Level Cooccurrence Matrix (GLCM) was one of the earliest techniques used for image texture analysis [19].
Gray level cooccurrence matrix (GLCM) [21] is commonly used to describe the surface textural factors of wear particles.
Chen [10] extracted low-order statistical features of road surface images including gray level cooccurrence matrix texture feature parameters and used linear discriminant function to determine the road surface state.