Asymmetric Gaussian Mixture Model Research Articles

Micro-Vibration Signal Denoising Algorithm of Spectral Morphology Fitting Based on Variational Mode Decomposition

Environmental micro-vibration has a significant impact on the proper functioning of semiconductor production and testing equipment such as the Czochralski growth furnace, polishing machine, photoetching machine, scanning electron microscope, etc. Low-frequency micro vibration has a significant influence on the normal operation of high-precision machining and testing equipment, and even causes irreversible damage to the equipment. Therefore, the micro-vibration test has important theoretical significance and engineering value for guiding the vibration isolation design of an electronic industrial workshop and ensuring the stable operation of various precision equipment in the workshop. As the observed acceleration signal is affected by noise introduced by the acceleration sensor itself, the signal processing circuit, the external power supply and interference from environmental factors, direct integration operations can lead to problems such as baseline drift and signal distortion in the calculation results. Aiming at the problem of noise interference in the micro-vibration measurement process, this paper proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition. The observed acceleration signal is decomposed into several orders of finite bandwidth intrinsic mode function components. The asymmetric Gaussian mixture model is used to complete the fitting of the spectral curves of each order of intrinsic mode function components. We can obtain the relevant parameters of the asymmetric Gaussian mixture model curves to complete the division of the effective information frequency band and finally achieve the denoising of the observed acceleration signal. Finally, the algorithm of this paper is compared with traditional denoising algorithms through numerical simulation examples and comparative experiments on denoising effects. The results show that the proposed algorithm has higher accuracy and anti-noise ability.

Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model

AbstractRetinal image registration, which is applied in diagnosing and treating eye diseases, plays an important role in medical image analysis. Existing methods suffer from problems due to different imaging viewpoints, times, quality, modalities, and retinal disasters. In this paper, we propose an efficient retinal images registration framework that overcomes these challenges without supervision. We present a layer‐wise matching method to achieve a uniform distribution of features in both image‐space and scale‐space. Then, a novel method called Bayesian integration is generated to accumulate more meaningful inputs. We use the results of different matches as priors, assign a score to each match, and categorize them using a dynamic threshold. Finally, in accordance with previous work, we transform the problem into a probabilistic model, with the asymmetric Gaussian mixture model representing the distribution. A robust estimation is performed on a non‐rigid transformation. The experimental results demonstrate that our proposed framework is robust to kinds of retinal image degradation and produces a more stable and accurate result than state‐of‐the‐art methods.

MASG-GAN: A multi-view attention superpixel-guided generative adversarial network for efficient and simultaneous histopathology image segmentation and classification

Efficient analysis of Haematoxylin and Eosin stained histopathology images has become a challenge in digital pathology work-flow. We propose a Multi-view Attention Superpixel-guided Generative Adversarial Network (MASG-GAN) to achieve the multi-task learning for nuclei segmentation and benign-malignant tissue classification. Firstly, a novel superpixel segmentation approach driven by Bounded Asymmetric Gaussian Mixture Model (BAGMM) is presented for generating superpixel-prior probability map with high-level semantics. Then, we develop a generator network that integrates student-branch and teacher-branch. Concretely, the teacher-branch takes superpixel-prior probability map as input and guides the student-branch for accurate segmentation and classification. Then in generator, we build a light-weight U-shaped block (LUB) that consists of depthwise separable convolutions with mini encoding-decoding structure to reduce network computational cost. Finally, a Multi-view Attention Module (MVAM) is designed for further enhance the segmentation quality of nuclei with small area and unclear boundary. Extensive experiments on five benchmark datasets demonstrate that our pipeline outperforms some state-of-the-art methods, especially in terms of efficiency.

A spatially constrained asymmetric Gaussian mixture model for image segmentation

The finite Gaussian mixture model (GMM) is a flexible and powerful tool for addressing many computer vision and pattern recognition problems. The Gaussian distribution is a probability distribution that is symmetric with respect to the mean. However, in many segmentation applications, the observed data obey an asymmetric distribution. Furthermore, the GMM is sensitive to imaging noise. To alleviate these issues, a new finite anisotropic asymmetric normal mixture model is presented in this paper. Note that GMM is a degraded case of our proposed model. First, the proposed model employs anisotropic spatial information to reduce the effect of imaging noise while preserving the details, such as edges, corners and slim structure objects. Second, the anisotropic spatial information is coupled into the skew normal distribution to fit the observed data obeying an asymmetric distribution. Then the modeling and estimation of the object intensity probability density function are proposed by using the anisotropic skew normal mixture model. The proposed model not only has the capability to fit the observed data obeying a non-symmetric distribution, but also can reduce the effect of noise while preserving the objects details. Finally, expectation maximization (EM) algorithm is adopted to estimate the model parameters in order to maximize the log-likelihood function. The experiment results on synthetic images and natural grayscale images demonstrate the superior performance of the proposed model compared with other state-of-the-art segmentation methods.

Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection

Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features. In this study, the authors develop a statistical self-refinement framework for the background subtraction task by using Dirichlet Process-based asymmetric Gaussian mixture model. The parameters of this model are learned using variational inference methods. They also incorporate feature selection simultaneously within the framework to avoid noisy influence from uninformative features. To validate the proposed framework, they report their results on background subtraction tasks on 8 different datasets for infrared and visible videos.

Image Denoising Based on the Asymmetric Gaussian Mixture Model

In recent years, image restoration has become a huge subject, and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results. The gaussian mixture model is the most common one. The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model (GMM). However, this approach is not entirely reasonable. It is well known that most natural images are complex and their distribution is not entirely gaussian. As a result, there are still many problems that GMM cannot solve. This paper tries to improve the finite mixture model and introduces the asymmetric gaussian mixture model into it. Since the asymmetric gaussian mixture model can simulate the asymmetric distribution on the basis of the gaussian mixture model, it is more consistent with the natural image data, so the denoising effect of the natural complex image is better. We carried out image denoising experiments under different noise scales and types, and found that the asymmetric gaussian mixture model has better denoising effect and performance.

A soft computing model based on asymmetric Gaussian mixtures and Bayesian inference

A novel unsupervised Bayesian learning framework based on asymmetric Gaussian mixture (AGM) statistical model is proposed since AGM is shown to be more effective compared to the classic Gaussian mixture model. The Bayesian learning framework is developed by adopting sampling-based Markov chain Monte Carlo (MCMC) methodology. More precisely, the fundamental learning algorithm is a hybrid Metropolis–Hastings within Gibbs sampling solution which is integrated within a reversible jump MCMC learning framework, a self-adapted sampling-based implementation, that enables model transfer throughout the mixture parameters learning process and therefore automatically converges to the optimal number of data groups. Furthermore, in order to handle high-dimensional vectors of features, a dimensionality reduction algorithm based on mixtures of distributions is included to tackle the irrelevant and extraneous features. The performance comparison between AGM and other popular models is given, and both synthetic and real datasets extracted from challenging applications such as intrusion detection, spam filtering and image categorization are evaluated to show the merits of the proposed approach.

A Rough Set Bounded Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation.

Accurate image segmentation is an important issue in image processing, where Gaussian mixture models play an important part and have been proven effective. However, most Gaussian mixture model (GMM) based methods suffer from one or more limitations, such as limited noise robustness, over-smoothness for segmentations, and lack of flexibility to fit data. In order to address these issues, in this paper, we propose a rough set bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation. First, based on our previous work where each cluster is characterized by three automatically determined rough-fuzzy regions, we partition the target image into three rough regions with two adaptively computed thresholds. Second, a new bounded indicator function is proposed to determine the bounded support regions of the observed data. The bounded indicator and posterior probability of a pixel that belongs to each sub-region is estimated with respect to the rough region where the pixel lies. Third, to further reduce over-smoothness for segmentations, two novel prior factors are proposed that incorporate the spatial information among neighborhood pixels, which are constructed based on the prior and posterior probabilities of the within- and between-clusters, and considers the spatial direction. We compare our algorithm to state-of-the-art segmentation approaches in both synthetic and real images to demonstrate the superior performance of the proposed algorithm.

A spatially constrained generative asymmetric Gaussian mixture model for image segmentation

Accurate image segmentation is an essential step in image processing, where Gaussian mixture models with spatial constraint play an important role. Nevertheless, most methods suffer from one or more challenges such as limited robustness to noise, over-smoothness for segmentations, and lack of flexibility to fit the observed data. To address these issues, in this paper, we propose a generative asymmetric Gaussian mixture model with spatial constraint for image segmentation. The asymmetric distribution is modified to be easily incorporated the spatial information. Then our asymmetric model can be constructed based on the posterior and prior probabilities of within-cluster and between-cluster. Based on the Kullback-Leibler divergence, we introduce two pseudo-likelihood quantities which consider the neighboring priors of within-cluster and between-cluster. Finally, we derive an expectation maximization algorithm to maximize the approximation of the data log-likelihood. We compare our algorithm with state-of-the-art segmentation approaches to demonstrate the superior performance of the proposed algorithm.

Simultaneous high-dimensional clustering and feature selection using asymmetric Gaussian mixture models

Finite mixture models are broadly applied in a wide range of applications concerning density estimation and clustering due to their sound mathematical basis and to the interpretability of their results. Indeed, they permit the incorporation of domain knowledge which allows the provision of better insight into the nature of the clusters and then uncovers application-specific desirable patterns that the practitioner is looking for. However, most of the works done on mixture models, when applied to computer vision tasks, assume that per-component data follow a mixture of Gaussians which may not hold as data are generally non-Gaussian (for instance, it is well-known that the distribution of natural images is highly non-Gaussian). The effect of the Gaussian mixture is analogous to the deployment of Euclidean or Mahalanobis type distances for discrimination purposes. Thus, this mixture cannot be applied efficiently in several applications involving asymmetric shapes. In this paper, we overcome this problem by using the asymmetric Gaussian mixture (AGM) model. The AGM can change its shape to model non-symmetrical and heavy tailed real world data which make it a good choice for modeling data with outliers. Modern computer vision applications generally generate complex high-dimensional data and usually, some features are noisy, redundant, or uninformative which may affect the speed and also compromise the accuracy of the used learning algorithm. Therefore, this paper addresses also the problem of unsupervised feature selection when considering AGM models. We propose two approaches for learning the resulting statistical framework. The first approach is based on the minimization of a message length objective and the second one considers rival penalized competitive learning. Our extensive simulations and experiments involving two challenging tasks namely visual scene categorization and facial expression recognition indicate that the method developed in this paper is efficient and has merits.