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Hybrid texture and gradient modeling for dynamic background subtraction identification systemin tobacco plant using 5G data service

Gowda Thirthe M.T., Chandrika J.

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 1

41--54

EN

Background: Detecting the plants as objects of interest in any vision-based input sequence is highly complex due to nonlinear background objects such as rocks, shadows,etc. Therefore, it is a difficult task and an emerging one with the development of precision agriculture systems. The nonlinear variations of pixel intensity with illuminationand other causes such as blurs and poor video quality also make the object detection taskchallenging. To detect the object of interest, background subtraction (BS) is widely usedin many plant disease identification systems, and its detection rate largely depends on thenumber of features used to suppress and isolate the foreground region and its sensitivitytoward image nonlinearity. Methodology: A hybrid invariant texture and color gradient-based approach is proposed to model the background for dynamic BS, and its performance is validated byvarious real-time video captures covering different kinds of complex backgrounds and various illumination changes. Based on the experimental results, a simple multimodal featureattribute, which includes several invariant texture measures and color attributes, yieldsfinite precision accuracy compared with other state-of-art detection methods. Experimental evaluation of two datasets shows that the new model achieves superior performanceover existing results in spectral-domain disease identification model. 5G assistance: After successful identification of tobacco plant and its analysis, the finalresults are stored in a cloud-assisted server as a database that allows all kinds of 5G servicessuch as IoT and edge computing terminals for data access with valid authentication fordetailed analysis and references.

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Intelligent velocity picking and uncertainty analysis based on the Gaussian mixture model

Wang Xiaowei, Gao Yang, Chen Chang, Yuan Huan, Yuan Sanyi

Acta Geophysica

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2022

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Vol. 70, no 6

2659--2673

EN

The stacking velocity is often obtained manually. However, manually picking is inefficient and is easily affected by subjective factors such as the priori information and the experience of different processors. To enhance its objectivity, efficiency and consistency, we investigated an unsupervised clustering intelligent velocity picking method based on the Gaussian mixture model (GMM). This method can automatically pick the stacking velocity fast, and provide uncertainty analysis as a quality control. Combined with the geometry feature of energy clusters in velocity spectra, taking advantages of the geometric diversity of energy clusters, GMM can ft the energy clusters with different distributions more appropriately. Then, mean values of the final several submodels are located as the optimal velocity, and the multiples are avoided under the expert knowledge and geological rules. In addition, according to the covariance of submodels, we can derive the uncertainty analysis of the final time-velocity pairs, so as to indicate the reliability of picking velocity at different depths. Moreover, the automated interpreted velocity field is used for both normal moveout (NMO) correction and stacking. The comparison with the manual references is adopted to evaluate the quality of the unsupervised clustering intelligent velocity picking method. Both synthetic data and 3D field data have shown that the proposed unsupervised intelligent velocity picking method can not only achieve similar accuracy with manual results, but also get rid of multiples. Furthermore, compared with manual picking, it can significantly improve the efficiency and accuracy in identifying pore and cave structures, as well as indicating the uncertainty of time-velocity pairs by variance.

3

Fitting a Gaussian mixture model through the Gini index

López-Lobato Adriana Laura, Avendaño-Garrido Martha Lorena

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 3

487--500

EN

A linear combination of Gaussian components is known as a Gaussian mixture model. It is widely used in data mining and pattern recognition. In this paper, we propose a method to estimate the parameters of the density function given by a Gaussian mixture model. Our proposal is based on the Gini index, a methodology to measure the inequality degree between two probability distributions, and consists in minimizing the Gini index between an empirical distribution for the data and a Gaussian mixture model. We will show several simulated examples and real data examples, observing some of the properties of the proposed method.

4

The Role of Climatic Variables for Estimating Probability of Abundance of Tree Species

Antúnez P., Wehenkel C., López-Sánchez C. A., Hernández-Díaz J. C.

Polish Journal of Ecology

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2017

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Vol. 65, nr 3

324--338

EN

We analyzed the influence of climatic variables on the abundance of native tree species in 1,490 sampling plots systematically distributed in the Sierra Madre Occidental (state of Durango, Northwestern Mexico, 26°50′ and 22°17′N and 107°09′ and 102°30′W). We used the Weibull distribution and the finite Gaussian mixture model to study the climatic limits of 15 tree species in relation to seven variables thought to affect species abundance. We found that although they may occur in the same geographical region, some species display a wider range of ecological tolerance than others. Of the 15 species under study, only two (Quercus magnoliifolia and Q. arizonica) can be considered generalists in relation to some climatic variables, while the other 13 species behaved as specialists, implying a narrower range of distribution. The analytical techniques used enabled us to demarcate the zones in which the probability of abundance of each species is highest in relation to the climate variables considered. The findings could be used to help define climate for the 15 studied tree species of economic and ecological interest.