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References in periodicals archive ?
The extracted features from training images were used to construct a Gaussian mixture model (GMM).
Abbreviations: GM, Gaussian Mixture model: LM, Location model: MLM, Modified Location model; EM, Expectation Maximization: p.d.f., probability density function.
Using all these frames together, i.e., n + k, we train a Gaussian Mixture Model on the keypoints features.
That is because the Gaussian mixture model is not adequately capable for classification in cases where the data are extremely approximate and there will be an extreme error rate.
In this experiment, we evaluate our method by cooperating with Gaussian mixture models (GMMs), which is a standard model for image representation, for image recognition.
In this work, we propose a supervised fusion where the classifiers (genuine or impostor) are trained using the match score densities and the parameters of the finite Gaussian mixture model that are used for modelling the genuine and impostor score densities of the training data.
To address the second issue, a clustering is performed using the nearest-neighbor clustering (NNC) method [16] to obtain the number of membership functions and a Gaussian mixture model (GMM) [17] is subsequently applied to determine the parameters associated with membership functions.
Fuzzy Local Gaussian Mixture Model for Brain MR Image Segmentation.
There are other kinds of methods for fire segmentation from images; among them the Gaussian mixture model (GMM) for sample distribution is an efficient one.
Prior research on background subtraction (BGS) used several parametric BGS techniques, such as running average [2-4], running Gaussian average [5-7], approximate median filter [7, 8], and Gaussian Mixture Model [9-11].
Keywords: Wireless sensor networks, localization algorithms, non-localizable problem, Gaussian mixture model, collaborative and predictive schemes
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