Analysis of dynamic textures using a 3D approach for the co-occurrence matrix features

2012 ◽  
Author(s):  
Muguras Mocofan ◽  
Florin Alexa
Keyword(s):  
Dynamic Textures ◽  
Occurrence Matrix

scholarly journals A 3D Approach to Co-Occurrence Matrix Features Used in Dynamic Textures Indexing

2013 ◽  
Vol 8-9 ◽  
pp. 516-526 ◽  
Author(s):  
Muguras Mocofan ◽  
Vasiu Radu
Keyword(s):  
Video Surveillance ◽  
Video Sequence ◽  
Medical Application ◽  
Dynamic Texture ◽  
Dynamic Textures ◽  
Temporal Features ◽  
Large Databases ◽  
3D Matrix ◽  
Indexing Model ◽  
Occurrence Matrix

The area of applications of dynamic texture is increasingly wide: video surveillance, transaction systems, medical application and video synthesis. The paper presents an indexing model in large databases of dynamics texture using the co-occurrence matrix features. The data from the video sequence that represents the dynamic texture are loaded in a 3D matrix. The application of the co-occurrence matrix is performed for each frame of the data parallelepiped covered in 3 directions. This enables/facilitates the integration of the temporal features of the dynamic texture in the mathematical description of the behaviour. Additionally, we use more translations to compute the indexing vector from the 2D+T space of dynamic textures.

Enhancement algorithm for color images based on co-occurrence matrix

2013 ◽  
Vol 32 (9) ◽  
pp. 2573-2575
Author(s):  
Bing-qing YANG ◽  
Xiao-ping TIAN ◽  
Cheng-mao WU
Keyword(s):  
Color Images ◽  
Occurrence Matrix ◽  
Enhancement Algorithm

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 Research supporting malaria control and elimination in China over four decades: a bibliometric analysis of academic articles published in chinese from 1980 to 2019

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yan-Qiu Du ◽  
Guo-Ding Zhu ◽  
Jun Cao ◽  
Jia-Yan Huang
Keyword(s):  
Drug Resistance ◽  
Bibliometric Analysis ◽  
Malaria Control ◽  
China National Knowledge Infrastructure ◽  
Malaria Research ◽  
The Past ◽  
Imported Case ◽  
Multidisciplinary Cooperation ◽  
Malaria Epidemic ◽  
Occurrence Matrix

Abstract Background China has accumulated considerable experience in malaria control and elimination over the past decades. Many research papers have been published in Chinese journals. This study intends to describe the development and experience of malaria control and elimination in China by quantitatively analysing relevant research using a bibliometric analysis. Methods A long-term, multistage bibliometric analysis was performed. Research articles published in Chinese journals from 1980 to 2019 were retrieved from the Wanfang and China National Knowledge Infrastructure (CNKI) databases. Year of publication, journal name and keywords were extracted by the Bibliographic Items Co-occurrence Matrix Builder (BICOMB). The K/A ratio (the frequency of a keyword among the total number of articles within a certain period) was considered an indicator of the popularity of a keyword in different decades. VOSviewer software was used to construct keyword co-occurrence network maps. Results A total of 16,290 articles were included. The overall number of articles continually increased. However, the number of articles published in the last three years decreased. There were two kinds of keyword frequency trends among the different decades. The K/A ratio of the keyword ‘Plasmodium falciparum’ decreased (17.05 in the 1980s, 13.04% in the 1990s, 9.86 in the 2000s, 5.28 in the 2010s), but those of ‘imported case’ and ‘surveillance’ increased. Drug resistance has been a continuous concern. The keyword co-occurrence network maps showed that the themes of malaria research diversified, and the degree of multidisciplinary cooperation gradually increased. Conclusions This bibliometric analysis revealed the trends in malaria research in China over the past 40 years. The results suggest emphasis on investigation, multidisciplinary participation and drug resistance by researchers and policymakers in malaria epidemic areas. The results also provide domestic experts with qualitative evidence of China’s experience in malaria control and elimination.

scholarly journals Analysis of 56,348 Genomes Identifies the Relationship between Antibiotic and Metal Resistance and the Spread of Multidrug-Resistant Non-Typhoidal Salmonella

2021 ◽  
Vol 9 (7) ◽  
pp. 1468
Author(s):  
Gavin J. Fenske ◽  
Joy Scaria
Keyword(s):  
Antibiotic Resistance ◽  
Foodborne Pathogen ◽  
Antibiotic Resistance Genes ◽  
Multidrug Resistant ◽  
Metal Resistance ◽  
Group 3 ◽  
Group 2 ◽  
Occurrence Matrix ◽  
Genome Assemblies ◽  
Group 1

Salmonella enterica is common foodborne pathogen that generates both enteric and systemic infections in hosts. Antibiotic resistance is common is certain serovars of the pathogen and of great concern to public health. Recent reports have documented the co-occurrence of metal resistance with antibiotic resistance in one serovar of S. enterica. Therefore, we sought to identify possible co-occurrence in a large genomic dataset. Genome assemblies of 56,348 strains of S. enterica comprising 20 major serovars were downloaded from NCBI. The downloaded assemblies were quality controlled and in silico serotyped to ensure consistency and avoid improper annotation from public databases. Metal and antibiotic resistance genes were identified in the genomes as well as plasmid replicons. Co-occurrent genes were identified by constructing a co-occurrence matrix and grouping said matrix using k-means clustering. Three groups of co-occurrent genes were identified using k-means clustering. Group 1 was comprised of the pco and sil operons that confer resistance to copper and silver, respectively. Group 1 was distributed across four serovars. Group 2 contained the majority of the genes and little to no co-occurrence was observed. Metal and antibiotic co-occurrence was identified in group 3 that contained genes conferring resistance to: arsenic, mercury, beta-lactams, sulfonamides, and tetracyclines. Group 3 genes were also associated with an IncQ1 class plasmid replicon. Metal and antibiotic co-occurrence from group 3 genes is mostly isolated to one clade of S. enterica I 4,[5],12:i:-.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

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