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Co-occurrence network analysis of modern Chinese poems
Physica A: Statistical Mechanics and its Applications, 2015Abstract A total of 606 co-occurrence networks of Chinese characters and words are constructed from rhymes, free verses, and prose poems. It is found that 98.5 % of networks have scale-free properties, while 19.8 % of networks do not have small-world features, especially the clustering coefficients in 5.6 % of networks are zero ...
Wei Liang +3 more
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NULL MODEL ANALYSIS OF SPECIES CO-OCCURRENCE PATTERNS
Ecology, 2000The analysis of presence-absence matrices with ''null model'' randomization tests has been a major source of controversy in community ecology for over two decades. In this paper, I systematically compare the performance of nine null model algorithms and four co-occurrence indices with respect to Type I and Type II errors.
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Temporal co-occurrence matrix approaches to motion analysis
SPIE Proceedings, 2004We propose a novel method for motion analysis in video sequences. It extends the co-occurrence matrix concept for texture analysis to the temporal domain. The approach proved to be versatile in the sense of targeting different motion analysis tasks.
A. Ukovich +2 more
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Generalized co-occurrence matrix for multispectral texture analysis
Proceedings of 13th International Conference on Pattern Recognition, 1996We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace method (ALSM).
M. Hauta-Kasari +3 more
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About the statistical analysis of co-occurrence
Computers and the Humanities, 1992Various objections are raised against current practice in co-occurrence analysis. The use of Yule's coefficient Y is then advocated.
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Co-occurrence-based texture analysis using irregular tessellations
Proceedings of 13th International Conference on Pattern Recognition, 1996Grey level co-occurrence features are one of the most powerful feature sets available for texture analysis. However, the moving window commonly employed to define the statistical scale at which the co-occurrence matrix is obtained assumes spatial stationarity of the underlying random field. This assumption is inappropriate in the case of natural images
F. Bello, R.I. Kitney
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Analysis of co-occurrence in a fungal community
Mycological Research, 1991An approach is presented for quantifying associations between species pairs over a scale of 0·5–1 mm by plating several 0·5–1 mm particles of washed organic matter on agar plates, and observing whether or not fungal species which emerge from the particles are distributed in a random manner with regard to each other. Most of the associations between the
D.A. Wardle, D. Parkinson
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Crowd density analysis using co-occurrence texture features
5th International Conference on Computer Sciences and Convergence Information Technology, 2010Crowd density analysis is crucial for crowd monitoring and management. This paper proposes a novel method for crowd density analysis. According to the framework, input images are firstly divided into patches, and each patch is associated with a density label based on its texture features.
null Wenhua Ma +2 more
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Research on Gambling in Young People: A Co-Occurrence Analysis
Journal of Gambling Studies, 2022Gambling as a risk factor in youth development, particularly its causes and consequences, has been the subject of a growing number of studies. However, the literature relating to young people has yet to be compiled in a systematic form. The present study adopts a descriptive bibliometric approach to map global research on gambling in young people using
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Conceptualization of place via spatial clustering and co-occurrence analysis
Proceedings of the 2009 International Workshop on Location Based Social Networks, 2009More and more users are contributing and sharing more and more contents on the Web via the use of content hosting sites and social media services. These user-generated contents are tagged with terms characterizing the contents from the users' perspectives.
Deng, D., Chuang, T., Lemmens, R.
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