Temat: model mieszaniny Gaussa - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Fitting a Gaussian mixture model through the Gini index
Autorzy:: López-Lobato, Adriana Laura
Avendaño-Garrido, Martha Lorena
Powiązania:: https://bibliotekanauki.pl/articles/2055144.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: Gini index problem
Gaussian mixture model
clustering
indeks Giniego
model mieszaniny Gaussa
grupowanie
Opis:: 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.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 487--500
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Hybrid texture and gradient modeling for dynamic background subtraction identification systemin tobacco plant using 5G data service
Autorzy:: Gowda Thirthe, M.T.
Chandrika, J.
Powiązania:: https://bibliotekanauki.pl/articles/38699145.pdf
Data publikacji:: 2023
Wydawca:: Instytut Podstawowych Problemów Techniki PAN
Tematy:: background subtraction
local binary pattern
tobacco plant
texture
Gaussian mixture model
illumination change
plant disease identification system
usuwanie tła
lokalny wzorzec binarny
tytoń
tekstura
model mieszaniny Gaussa
zmiana oświetlenia
system identyfikacji chorób roślin
Opis:: 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.
Źródło:: Computer Assisted Methods in Engineering and Science; 2023, 30, 1; 41-54
2299-3649
Pojawia się w:: Computer Assisted Methods in Engineering and Science
Dostawca treści:: Biblioteka Nauki

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