In this paper, a coverless information hiding algorithm is introduced. In which, the grayscale gradient co-occurrence matrix is used to encode images and the mapping relationship between the images and the random numbers is used to express the payload information. There are three steps for this ...
In this paper, a texture feature based bark classification method is presented. Our method uses two types of texture features: the co-occurrence matrix feature and the long connection length emphasis (LCLE) feature, which is extracted from the binary bark image. For the extraction of binary text...
The present work proposes a novel patch-based synthesis algorithm for synthesizing new textures that employs the powerful concept of gray-level co-occurrence matrix coupled with restricted cross-correlation. Furthermore, a simple and peculiar blending mechanism has been devised which avoids the necessity...
In order to play the role of co-occurrence matrix inertia in the analysis and retrieval of image texture efficiently,a new expanded order co-occurrence matrix inertia based on interval grayscale compression is studied. One part of the grayscale information of the original image is compressed and...
The Gray Level Co-occurrence Matrix (GLCM) was employed to extract and analyze the grayscale and texture features of the strain cloud maps, facilitating a quantitative assessment of their evolution. The aim was to pinpoint the precursor characteristics associat...
First-order and gray-level co-occurrence matrix second-order texture features were extracted. Multivariate logistic regression was performed to assess for predictors of malignancy (STATA v16.1). RESULTS. One hundred forty-seven cases with complete SWE data were selected for analysis (mean age 54.3,...
Damage classification of concrete structures based on grey level co-occurrence matrix using Haar\’s discrete wavelet transform. Comput. Concr. 2007, 4, 243–257. [Google Scholar] [CrossRef] Yin, Y.; Fan, Y.; Ning, W. Research on the Prediction of Mechanical Response to Concrete under ...
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There is an extraordinary step of calculating the two-dimensional FFT related to the size of the matrix processed. In this case, it is related to the size of the image, thus: 𝑑𝑡+1dt+1 multiplied by t, where t is depth threshold, and d is maximal degree. As two-dimensional FFT...