Results 21 to 30 of about 365,701 (280)
Superpixel Segmentation Using Gaussian Mixture Model [PDF]
IEEE Transactions on Image Processing, 2018 Superpixel segmentation algorithms are to partition an image into perceptually coherence atomic regions by assigning every pixel a superpixel label. Those algorithms have been wildly used as a preprocessing step in computer vision works, as they can enormously reduce the number of entries of subsequent algorithms.
Zhihua Ban, Jianguo Liu, Li Cao
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Fitting a Gaussian Mixture Model Through the Gini Index
International Journal of Applied Mathematics and Computer Science, 2021 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 ...
López-Lobato Adriana Laura +1 more
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Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing. [PDF]
PLoS ONE, 2017 This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation.
Siow Hoo Leong, Seng Huat Ong
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Extensible Gaussian Mixture Model for Image Prior Modeling [PDF]
Jisuanji gongcheng, 2020 To address the inextensible fixed number of components in image prior modeling based on Gaussian Mixture Model(GMM),this paper proposes an extensible GMM model based on Dirichlet Process(DP).Through the addition and merging mechanism of cluster ...
ZHANG Mohua, PENG Jianhua
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Mixtures of Shifted Asymmetric Laplace Distributions [PDF]
, 2012 A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the general inverse Gaussian ...
Browne, Ryan P. +2 more
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Scale-Based Gaussian Coverings: Combining Intra and Inter Mixture Models in Image Segmentation
Entropy, 2009 By a “covering” we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model.
Jean-Luc Starck +2 more
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Variational Autoencoder With Optimizing Gaussian Mixture Model Priors
IEEE Access, 2020 The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem.
Chunsheng Guo +5 more
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Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
Sensors, 2016 In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking.
Qian Zhang, Taek Lyul Song
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Infinite Mixtures of Multivariate Gaussian Processes [PDF]
, 2013 This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning.
Sun, Shiliang
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Ensemble image registration by a spatially constrained clustering approach
International Journal of Advanced Robotic Systems, 2016 In this article, a novel spatially constrained clustering approach is proposed for ensemble image registration. We use a spatially constrained Gaussian mixture model, which is based on a joint Gaussian mixture model and Markov random field, to model the ...
Hao Zhu +3 more
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