Non-subsampled contourlets and gray level co-occurrence matrix based images segmentation

2011 ◽  
Author(s):  
Zhang Jian ◽  
Chen Xiaowei
Keyword(s):  
Gray Level ◽  
Occurrence Matrix

Detection of image region-duplication forgery based on gray level co-occurrence matrix

2012 ◽  
Vol 31 (6) ◽  
pp. 1628-1630
Author(s):  
Jia-jia OU ◽  
Bi-ye CAI ◽  
Bing XIONG ◽  
Feng LI
Keyword(s):  
Gray Level ◽  
Image Region ◽  
Region Duplication ◽  
Occurrence Matrix

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

scholarly journals The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

ICT Express ◽  
2021 ◽  
Author(s):  
Fitri Utaminingrum ◽  
Syam Julio A. Sarosa ◽  
Corina Karim ◽  
Femiana Gapsari ◽  
Randy Cahya Wihandika
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Gray Level ◽  
Occurrence Matrix

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

2021 ◽  
pp. 1-7
Author(s):  
Lazar M. Davidovic ◽  
Jelena Cumic ◽  
Stefan Dugalic ◽  
Sreten Vicentic ◽  
Zoran Sevarac ◽  
...  
Keyword(s):  
Neural Network ◽  
Saccharomyces Cerevisiae ◽  
Random Trees ◽  
Computational Method ◽  
Yeast Cells ◽  
Matrix Analysis ◽  
Gray Level ◽  
Cell Nuclei ◽  
Intact Cells ◽  
Occurrence Matrix

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

Research on Anisotropic Fracture Mechanical Behavior of Cortical Bone with Combined Method of Fractal and Gray Level Co-Occurrence Matrix

2021 ◽  
Vol 11 (1) ◽  
pp. 67-75
Author(s):  
Dagang Yin ◽  
Bin Chen ◽  
Huifen Zhou
Keyword(s):  
Mechanical Properties ◽  
Fracture Surface ◽  
Cortical Bone ◽  
Fracture Mechanisms ◽  
Gray Level ◽  
Combined Method ◽  
Characteristic Parameters ◽  
Fracture Surfaces ◽  
Anisotropic Fracture ◽  
Occurrence Matrix

The irregular fracture surface of cortical bone, which is caused by complex multilevel micro-nanostructure, reflects the mechanical properties and fracture mechanisms. It is of great significance to characterize some characteristic parameters from the fracture surfaces of bone. In this research, anisotropic fracture mechanical properties of bovine femoral cortical bone along transverse, longitudinal and radial direction are firstly obtained by three-point bend experiment. Then the fracture routes and fracture surfaces are observed by scanning electron microscope. The observation shows that the formed fracture surfaces, which are caused by different crack routes, are extremely rough and have complex textures. Lastly, the combined method of fractal and gray level co-occurrence matrix are adopted to describe the morphology of fracture surface of cortical bone objectively and quantitatively. It is shown that the fracture surface of cortical bone has obvious fractal characteristics and four statistical texture feature parameters (contrast,angular second moment, correlation and entropy) of GLCM of fracture surfaces can describe a certain fracture texture character. The relationship between the characteristic parameters and macroscopic mechanical properties are established. The quantitative analysis and automatic class identification for the fracture surfaces of cortical bone can be achieved.

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