How to accurately register point sets still remains a challenging task, due to some unfavorable factors. In this article, a robust point set registration approach is proposed based on the Gaussian mixture model (GMM) ...
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How to accurately register point sets still remains a challenging task, due to some unfavorable factors. In this article, a robust point set registration approach is proposed based on the Gaussian mixture model (GMM) with multiple effective constraints. The GMM is established by wrapping a model point set to a target point set, via a spatial transformation. Instead of a displacement model, the spatial transformation is decomposed as two types of transformations, an affine transformation and a nonaffine deformation. For the affine transformation, a constraint term of the parameter vector is applied to improve the robustness and efficiency. In order to enforce the smoothness, the square norm of the kernel Hilbert space is adopted as a coherent constraint for the nonaffine deformation. Moreover, the manifold regularization is utilized as a constraint in the proposed model, to capture the spatial geometry of point sets. In addition, the expectation-maximization algorithm is developed to solve the unknown variables of the proposed model. Compared to the state-of-the-art approaches, the proposed model is more robust to deformation and rotation, due to the use of multiple effective constraints. Experimental results on several widely used data sets demonstrate the effectiveness of the proposed model.
We report some recent algorithmic refinements and the resulting simulated and real image reconstructions of fluorescence micrographs by using a blind-deconvolution algorithm based on maximum-likelihood estimation. Bli...
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We report some recent algorithmic refinements and the resulting simulated and real image reconstructions of fluorescence micrographs by using a blind-deconvolution algorithm based on maximum-likelihood estimation. Blind-deconvolution methods encompass those that do not require either calibrated or theoretical predetermination of the point-spread function (PSF). Instead, a blind deconvolution reconstructs the PSF concurrently with deblurring of the image data. Two-dimensional computer simulations give some definitive evidence of the integrity of the reconstructions of both the fluorescence concentration and the PSF. A reconstructed image and a reconstructed PSF from a two-dimensional fluorescent data set show that the blind version of the algorithm produces images that are comparable with those previously produced by a precursory nonblind version of the algorithm. They furthermore show a remarkable similarity, albeit not perfectly identical, with a PSF measurement taken for the same data set, provided by Agard and colleagues. A reconstructed image of a three-dimensional confocal data set shows a substantial axial smear removal. There is currently an existing trade-off in using the blind deconvolution in that it converges at a slightly slower rate than the nonblind approach. Future research, of course, will address this present limitation.
In this article, we make estimation and prediction inferences for the generalized half normal distribution. The maximum likelihood and Bayes estimators of unknown parameters are obtained based on hybrid Type I censore...
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In this article, we make estimation and prediction inferences for the generalized half normal distribution. The maximum likelihood and Bayes estimators of unknown parameters are obtained based on hybrid Type I censored samples. We obtain asymptotic intervals using the observed Fisher information matrix and also construct bootstrap intervals of unknown parameters. Bayes estimators are obtained under the squared error loss function using different approximation methods. We also construct the highest posterior density intervals of unknown parameters. Further one- and two-sample predictors and prediction intervals of censored observations are discussed. A Monte Carlo simulation study is conducted to compare the performance of the proposed methods. We further analyze a real data set for illustrative purposes. Finally, conclusions are presented.
The phenomenon of toll evasion on expressway is universal and recognition of evasion phenomena has become a necessary means to reduce property losses. In view of this situation, a clustering method based Gaussian mixt...
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The phenomenon of toll evasion on expressway is universal and recognition of evasion phenomena has become a necessary means to reduce property losses. In view of this situation, a clustering method based Gaussian mixture model(GMM) of load weight is applied to identify the toll evasion by transportation vehicles. Firstly, based on the Kolmogorov-Smirnov and Quantile-Quantile plot test of the load weight in different driving cycles, it is definite that the load in a certain driving cycle is approximately Gaussian mixture distribution(GMD) and there are significant differences among load distributions in different driving cycles. Then, the load during historical vehicles in a certain driving cycle is clustered by GMM. expectation-maximization(EM) algorithm is used to calculate the parameters of GMM. Based on the clustering results, the GMD of the load in a certain driving cycle is clear. Finally, according to the criterion of Gaussian distribution, we scientifically obtain a reasonable load interval and distinguish the toll evasion by transportation vehicles. In addition, the concrete practice procedures of the toll evasion recognition method are discussed. Empirical results demonstrate that could achieve satisfactory results when the proposed method is applied to model the toll data of six-axis trucks of south of Baoding City station in Hebei province of China.
This paper proposes a robust procedure for solving multiphase regression problems that is efficient enough to deal with data contaminated by atypical observations due to measurement errors or those drawn from heavy-ta...
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This paper proposes a robust procedure for solving multiphase regression problems that is efficient enough to deal with data contaminated by atypical observations due to measurement errors or those drawn from heavy-tailed distributions. Incorporating the expectation and maximizationalgorithm with the M-estimation technique, we simultaneously derive robust estimates of the change-points and regression parameters, yet as the proposed method is still not resistant to high leverage outliers we further suggest a modified version by first moderately trimming those outliers and then implementing the new procedure for the trimmed data. This study sets up two robust algorithms using the Huber loss function and Tukey's biweight function to respectively replace the least squares criterion in the normality-based expectation and maximizationalgorithm, illustrating the effectiveness and superiority of the proposed algorithms through extensive simulations and sensitivity analyses. Experimental results show the ability of the proposed method to withstand outliers and heavy-tailed distributions. Moreover, as resistance to high leverage outliers is particularly important due to their devastating effect on fitting a regression model to data, various real-world applications show the practicability of this approach. (C) 2019 Elsevier Inc. All rights reserved.
A unified framework for a fully automated diagnostic system for cervical intraepithelial neoplasia (CIN) is proposed. CIN is a detectable and treatable precursor pathology of cancer of the uterine cervix. algorithms b...
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A unified framework for a fully automated diagnostic system for cervical intraepithelial neoplasia (CIN) is proposed. CIN is a detectable and treatable precursor pathology of cancer of the uterine cervix. algorithms based on mathematical morphology, and clustering based on Gaussian mixture modeling (GMM) in a joint color and geometric feature space, are used to segment macro regions. A non-parametric technique, based on the transformation and analysis of the D(R) (distortion-rate) curve is proposed to assess the model order. This technique provides good starting points to infer the GMM parameters via the expectation-maximization (EM) algorithm, reducing the segmentation time and the chances of getting trapped in local optima. The classification of vascular abnormalities in CIN, such as mosaicism and punctations, is modeled as a texture classification problem, and a solution is attempted by characterizing the neighborhood gray-tone dependences and co-occurrence statistics of the textures. The model presented in this paper provides a sequential framework for translating digital images of the cervix into a complete diagnostic tool, with minimal human intervention. In its current form, the research presented in this work may be used to aid physicians to locate abnormalities due to CIN and assess the best areas for a biopsy.
Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. expectation-Ma...
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Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. (c) 2004 Elsevier Ltd. All rights reserved.
Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is on...
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Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.
In this article we propose a robust probabilistic multivariate calibration (RPMC) model in an attempt to identify linear relationships between two sets of observed variables contaminated with outliers Instead of the G...
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In this article we propose a robust probabilistic multivariate calibration (RPMC) model in an attempt to identify linear relationships between two sets of observed variables contaminated with outliers Instead of the Gaussian assumptions that predominate in classical statistical models, RPMC is closely related with the multivariate Student t-distribution over noises and latent variables. Thus RPMC diminishes the effect Of Outlying data points by regulating the thickness of the distribution tails. RPMC is essentially a robustified version of the supervised probabilistic principal component analysis (SPPCA) that has emerged recently, We show that RPMC encompasses probabilistic principal component analysis and SPPCA as limiting cases. We also derive an efficient EM algorithm for parameter estimation in RPMC. Based on a probabilistic description of latent variables, we present a procedure for the detection of Outliers. The experimental results from both simulated examples and real life data sets demonstrate the effectiveness and robustness of our proposed approach.
Estimation of parameters of the generalized inverse Lindley (GIL) distribution is considered under a hybrid censoring scheme. The point estimators, such as the maximum likelihood estimators using the expectation-Maxim...
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Estimation of parameters of the generalized inverse Lindley (GIL) distribution is considered under a hybrid censoring scheme. The point estimators, such as the maximum likelihood estimators using the expectation-maximization (E-M) algorithm, have been derived. The two approximate Bayes estimators using Tierney and Kadane's method and Gibbs sampling procedure, using the gamma prior and the general entropy loss (GEL) function, have been obtained. Several confidence intervals are proposed, such as the asymptotic confidence intervals (ACIs), bootstrap confidence intervals, and the highest posterior density (HPD) credible intervals. The prediction for future observations has been considered under one and two-sample Bayesian prediction methods using the type-i hybrid censoring scheme. An extensive simulation study has been conducted to numerically evaluate all the estimators' performances. The point estimators are compared through their biases and mean squared errors (MSEs). The performances of confidence intervals are evaluated using coverage probability (CP), average length (AL), and probability coverage density (PCD). Two real-life datasets have been considered for illustrative purposes.
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