Dynamic texture (DT) provides a flexible and suitable tool for representing phenomena over space and time. We focus here on DT learning for multi-view domains, each of which is sufficient to learn the target concept. We make several contributions in this paper. First, we derive new features and then present their use in our description of DT. Second, we introduce multi-view dynamic texture learning, which aims to combine several views to achieve a more reliable and accurate result. The core of this multi-view combination is based on the recent information theories of the probabilistic rand index. Third, we present a way to impose spatial smoothness constraints between neighboring observations. Finally, we empirically show that our model outperforms existing state-of-the-art methods recently proposed in the literature on various datasets.

The study of phenomena segmentation in natural scenes has attracted growing attention and is a popular research topic. While there are many studies detailing algorithms for motion segmentation in dynamic scenes, an important question arising from these studies is how to combine these algorithms. How can the label correspondence problem be resolved? Answering this question is difficult, because there are no labeled training data available in clustering to guide the search. Also, different algorithms produce incompatible data labels resulting in intractable correspondence problems. This paper presents a new consensus model for motion segmentation in dynamic scenes, which aims to combine several unsupervised methods to achieve a more reliable and accurate result. The advantage of our method is that it is intuitively appealing. Numerical experiments on various phenomena are conducted. The performance of the proposed model is compared with the best state-of-the-art motion segmentation methods recently proposed in the literature, demonstrating the robustness and accuracy of our method.

Fuzzy c-means (FCM) clustering has been successfully applied in various pattern recognition areas. While FCM is gaining attention, an important issue arising from these studies is the need to determine which attributes of the data should be used. Answering this question is difficult, because there is no labeled training data available in clustering to guide the search. We present a feature selection for FCM. The advantage of our method is that it is intuitively appealing, avoiding combinatorial searches, and allowing us to prune the feature set. Our method is also adaptable and can change through complex scenes in an online environment. We do not have to wait until all data have been generated before learning begins. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback-Leibler divergence information. Numerical experiments are presented to demonstrate the robustness and accuracy of our method.

The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature.

A mixture model based on the symmetric Gaussian distribution that simultaneously treats the feature selection, and the model detection has recently received great attention for pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and nonsymmetric form. This brief presents a new asymmetric mixture model for model detection and model selection. In this brief, the proposed asymmetric distribution is modeled with multiple student's- t distributions, which are heavily tailed and more robust than Gaussian distributions. Our method has the flexibility to fit different shapes of observed data, such as non-Gaussian and nonsymmetric. Another advantage is that the proposed algorithm, which is based on the variational Bayesian learning, can simultaneously optimize over the number of the student's- t distribution that is used to model each asymmetric distribution, the number of components, and the saliency of the features. Numerical experiments on both synthetic and real-world datasets are conducted. The performance of the proposed model is compared with other mixture models, demonstrating the robustness, accuracy, and effectiveness of our method.

Bayesian detection for the symmetric Gaussian mixture model has recently received great attention for pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and non-symmetric form. This study presents a new asymmetric mixture model for model detection. In this paper, the proposed asymmetric distribution is modeled with multiple Student's-t distributions, which are heavily tailed and more robust than Gaussian distributions. Our method has the flexibility to fit different shapes of observed data such as non-Gaussian and non-symmetric. Another advantage is that the proposed algorithm, which is based on the variational Bayesian learning, can simultaneously optimize over the number of the Student's-t distribution that is used to model each asymmetric distribution, and the number of components. The performance of the proposed model is compared to other mixture models, demonstrating the robustness, accuracy, and effectiveness of our method.

The Dirichlet process mixture model (DPMM) with spatial constraints – e.g. hidden Markov random field (HMRF) model – has been considered as an effective algorithm for image processing application. However, the HMRF model is complex and time‐consuming for implementation. A new DPMM has been introduced, where a generalised mean (GDM) is selected as the spatial constraints function. The GDM is applied not only on prior probability (and posterior probability) to incorporate local spatial information and component information, but also on conditional probability to incorporate local spatial information and observation information. The purpose of the HMRF model and GDM are the same for incorporating some spatial constraints into the system. However, compared to HMRF, GDM is easier, faster and simpler to implement. Finally, a variational Bayesian approach has been adopted for parameters estimation and model selection. Experimental results on image segmentation application demonstrate the improved performance of the proposed approach.

Fuzzy c‐means (FCMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well‐known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image segmentation applications. In this study, the authors propose a new algorithm to incorporate the merits of these two approaches and reveal some intrinsic relationships between them. In the authors model, the new objective function pays more attention on spatial constraints and adopts Gaussian distribution as the distance function. Thus, their model can degrade to the standard GMM as a special case. Our algorithm is fully free of the empirically pre‐defined parameters that are used in traditional FCM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in their algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighbourhood to incorporate the local spatial information and intensity information. Finally, they utilise the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. The mean template is considered as a spatial constraint for collecting more image spatial information. Compared with HMRF, their method is simple, easy and fast to implement. The performance of their proposed algorithm, compared with state‐of‐the‐art technologies including extensions of possibilistic fuzzy c‐means (PFCM), GMM, FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.

The finite mixture model based on the Student's-t distribution, which is heavily tailed and more robust than the Gaussian mixture model (GMM), is a flexible and powerful tool to address many computer vision and pattern recognition problems. However, the Student's-t distribution is unbounded and symmetrical around its mean. In many applications, the observed data are digitalized and have bounded support. The distribution of the observed data usually has an asymmetric form. A new finite bounded asymmetrical Student's-t mixture model (BASMM), which includes the GMM and the Student's-t mixture model (SMM) as special cases, is presented in this paper. We propose an extension of the Student's-t distribution in this paper. This new distribution is sufficiently flexible to fit different shapes of observed data, such as non-Gaussian, nonsymmetric, and bounded support data. Another advantage of the proposed model is that each of its components can model the observed data with different bounded support regions. In order to estimate the model parameters, previous models represent the Student's-t distributions as an infinite mixture of scaled Gaussians. We propose an alternate approach in order to minimize the higher bound on the data negative log-likelihood function, and directly deal with the Student's-t distribution. As an application, our method has been applied to image segmentation with promising results.

Synthetic aperture radar (SAR) data are often affected by speckle noise, which originates in the SAR system's coherent nature. In this paper, we introduce a simple and effective algorithm to make the traditional Student's t-mixture model (SMM) more robust to noise. The proposed new modified SMM (MSMM) is applied for SAR image segmentation. SMM has come to be regarded as an alternative to the Gaussian mixture model (GMM) as it is heavy tailed and more robust to outliers. However, a major shortcoming of this method is that it does not take into account the spatial dependencies in the image. Although some existing methods incorporate the spatial relationship between neighboring pixels, they are still not robust enough to noise. The advantages of our method are as follows. First, we introduce MSMM to incorporate the local spatial information and pixel intensity value by considering the conditional probability of an image pixel influenced by the probabilities of pixels in its immediate neighborhood. Furthermore, we introduce the additional parameter α to control the extent of this influence. The larger α indicates the heavier extent of influence in the neighborhoods. Second, the prior probability of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood, which incorporates local spatial and component information. Third, our model is based on the finite mixture model (FMM); it is simple and easy to implement, and the expectation maximization algorithm can be applied for estimation of optimal parameters. Finally, the traditional SMM can be considered as a special case of our model. Thus, our method is general enough for FMM-based techniques. Experimental results on both simulated and real SAR images demonstrate the improved robustness and effectiveness of our approach.

The proposed work is targeted toward improving the Gaussian mixture model (GMM) for the background suppression-based moving object detection. The GMM has been widely used for moving object detection due to its high applicability. However, the GMM cannot properly model noisy or nonstationary backgrounds and fails to discriminate between the foreground and background modes. The extensions to GMM provide increased accuracy in expense of complex implementation and reduced applicability. In response, this work proposes two simple improvements: 1) a novel distance measure based on local support weights and histogram of gradients to provide distinct cluster values; and 2) use of background layer concept to properly segment the foreground. The method also uses variable number of clusters for generalization. The main advantages of the method are implicit use of pixel relationships through distance measure with least modification to the conventional GMM and effective background noise removal through the use of background layer concept with no postprocessing involved. The extensive experimentations on various types of video sequences are performed to validate the improvement in accuracy compared to the GMM and a number of state-of-the-art methods.

The automatic clustering of time-varying characteristics and phenomena in natural scenes has recently received great attention. While there exist many algorithms for motion segmentation, an important issue arising from these studies concerns that for which attributes of the data should be used to cluster phenomena with a certain repetitiveness in both space and time. It is difficult because there is no knowledge about the labels of the phenomena to guide the search. In this paper, we present a feature selection dynamic mixture model for motion segmentation. The advantage of our method is that it is intuitively appealing, avoiding any combinatorial search, and allowing us to prune the feature set. Numerical experiments on various phenomena are conducted. The performance of the proposed model is compared with that of other motion segmentation algorithms, demonstrating the robustness and accuracy of our method.

Segmentation of a medical image based on the modeling and estimation of the tissue intensity probability density functions via a Gaussian mixture model has recently received great attention. However, the Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, nonsymmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Our method is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3- D medical ones.

The Gaussian Mixture Model (GMM) with a spatial constraint, e.g. a Hidden Markov Random Field (HMRF), has been proven effective for image segmentation. However, the determination of parameter β in the HMRF model is, in fact, noise dependent to some degree. In this paper, we propose a simple and effective algorithm to make the traditional Gaussian Mixture Model more robust to noise, with consideration of the relationship between the local spatial information and the pixel intensity value information. The conditional probability of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood to incorporate the spatial and intensity information. In this case, the parameter β can be assigned to a small value to preserve image sharpness and detail in non-noise images. At the same time, the neighborhood window is used to tolerate the noise for heavy-noised images. Thus, the parameter β is independent of image noise degree in our model. Finally, our algorithm is not limited to GMM-it is general enough so that it can be applied to other distributions based on the construction of the Finite Mixture Model (FMM) technique.

The finite mixture model based on the Student's-t distribution, which is heavily tailed and more robust than the Gaussian mixture model (GMM), is a flexible and powerful tool to address many pattern recognition problems. However, the Student's-t distribution is unbounded. In many applications, the observed data are digitalized and have bounded support. A new finite multivariate Student's-t mixture model for bounded support data, which includes the GMM and the Student's-t mixture model (SMM) as special cases, is presented in this paper. We propose an extension of the Student's-t distribution in this paper. This new distribution is sufficiently flexible to fit different shapes of observed data, such as non-Gaussian, non-symmetric, and bounded support data. Another advantage of the proposed model is that each of its components can model the observed data with different bounded support regions. In order to estimate the model parameters, previous models represent the Student's-t distributions as an infinite mixture of scaled Gaussians. We propose an alternate approach in order to minimize the higher bound on the data negative log-likelihood function, and directly deal with the Student's-t distribution.

Segmentation of medical image based on the modeling and estimation of the tissue intensity probability density functions via Gaussian mixture model (GMM) has recently received great attention. However, Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, non-symmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Our method is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3D medical ones.

This paper presents a new finite mixture model for image segmentation. First, in order to take into account the spatial dependencies in an image, existing mixture models use a constant temperature parameter (β) throughout the image for every label. The constant value of β reduces the impact of noise in homogeneous regions but negatively affects segmentation along the border of two regions. We propose a new way to use a different value of β throughout the image. Secondly, in order to incorporate the correlation between each centre pixel and its neighboring pixels, existing mixture model gives the same importance to all pixels in a neighborhood window. We assign different weights to different pixels appearing in the window, which is based on the fact that the clique strength should be reduced with distance. Thirdly, our model is based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian. We exploit Dirichlet distribution and Dirichlet law to incorporate the spatial relationships between pixels in an image. Finally, expectation maximization (EM) algorithm is adopted to maximize the data log-likelihood and to optimize the parameters. The performance is compared to other existing models based on the model-based techniques, demonstrating superiority of the proposed model for image segmentation.

Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. In this paper, we propose a new algorithm to incorporate the local spatial information with the consideration of mean template. Our algorithm is fully free of the empirically predefined parameters that are used in other FCM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in our algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighborhood to incorporate the local spatial information and intensity information. Finally, we utilize the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. Compared to HMRF, our method is simple, easy and fast to implement.

Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it lacks of sufficient robustness to image noise. In this paper, we propose a simple and effective method to make the traditional FCM more robust to noise, with the help of generalized mean. Traditional FCM can be considered as a linear combination of membership and distance (function) from the expression of its mathematical formula. The proposed generalized FCM (GFCM) is generated by applying generalized mean on these two items. We impose generalized mean on membership to incorporate local spatial information and cluster information, and on distance function to incorporate local spatial information and observation information (image intensity value). Thus, our GFCM is more robust to image noise with the spatial constraints: the generalized mean. The performance of our proposed algorithm, compared with state-of-the-art technologies including modified FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.

In this study, the authors introduce a new weighted Student's t ‐mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t ‐distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t ‐mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well‐known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model‐based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach.

The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications; however, recently the Student's t ‐mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t ‐mixture distribution as its hidden state is the Student's t ‐hidden Markov model (SHMM). The authors propose a novel Student's t ‐hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation–maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.

In this paper, we propose a novel algorithm for feature selection and model detection using Student's t-distribution based on the variational Bayesian (VB) approach. First, our method is based on the Student's t-mixture model (SMM) which has heavier tail than the Gaussian distribution and is therefore less sensitive to small numbers of data points and consequent precision-estimates of the components number. Second, the number of components, the local feature saliency and the parameters of the mixture model are simultaneously estimated by Bayesian variational learning. Experimental results using synthetic and real data demonstrate the improved robustness of our approach.

Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. Student's t-distribution has come to be regarded as an alternative to Gaussian distribution, as it is heavily tailed and more robust for outliers. In this letter, we propose a new algorithm to incorporate the merits of these two approaches. The advantages of our method are as follows: First, we incorporate the local spatial information and pixel intensity value by considering the labeling of an image pixel influenced by the labels in its immediate neighborhood. Second, we introduce additional parameterato control the extent of this influence. The largeraindicates heavier extent of influence in the neighborhoods. Finally, we utilize a mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. Compared with HMRF, our method is simple, easy and fast to implement. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of our approach.

In this paper, a new mixture model for image segmentation is presented. We propose a new way to incorporate spatial information between neighboring pixels into the Gaussian mixture model based on Markov random field (MRF). In comparison to other mixture models that are complex and computationally expensive, the proposed method is fast and easy to implement. In mixture models based on MRF, the M-step of the expectation-maximization (EM) algorithm cannot be directly applied to the prior distribution πijfor maximization of the log-likelihood with respect to the corresponding parameters. Compared with these models, our proposed method directly applies the EM algorithm to optimize the parameters, which makes it much simpler. Experimental results obtained by employing the proposed method on many synthetic and real-world grayscale and colored images demonstrate its robustness, accuracy, and effectiveness, compared with other mixture models.

Finite mixture models with symmetric distribution have been widely used for many computer vision and pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and nonsymmetric form. This paper presents a new nonsymmetric mixture model for image segmentation. The advantage of our method is that it is simple, easy to implement, and intuitively appealing. In this paper, each label is modeled with multiple D-dimensional Student's t-distribution, which is heavily tailed and more robust than Gaussian distribution. Expectation-maximization algorithm is adopted to estimate model parameters and to maximize the lower bound on the data log-likelihood from observations. Numerical experiments on various data types are conducted. The performance of the proposed model is compared with that of other mixture models, demonstrating the robustness, accuracy, and effectiveness of our method.

Dynamic textures are common in natural scenes and have recently received great attention in video content analysis. A dynamic fuzzy clustering to automatically segment time-varying characteristics and phenomena is presented in this paper. First, compared with the existing models that assume a common prior distribution, which independently generates the labels, the prior distribution in our model is different for each observation and depends on the labels. In addition, in order to properly account for the neighboring observations during the learning step, we introduce the explicit assumptions of the hidden Markov random field model into the dynamic fuzzy clustering. Second, in order to model the observed dynamic texture data, only grayscale information is taken into consideration of the existing models. We use different visual properties by proposing a new distribution in this paper. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback–Leibler divergence information. Numerical experiments are presented, where the proposed model is tested on various simulated and real dynamic textures.

This paper aims toward improving background suppression from video frames by incorporating multiresolution features in Gaussian mixture model (GMM). GMM has proven its place for background modeling due to its better applicability and robustness compared with other popular methods in literature. However, GMM fails in a number of situations such as noisy and non-stationary background, slow foregrounds, and illumination variation. Extensions to GMM have also been proposed to increase accuracy in expense of increased complexity, decrease in execution speed, and reduced applicability. In view of the above, this paper aims to provide a methodology to assimilate useful multiresolution features with GMM that considerably improves the performance. The contributions of this paper are: 1) a novel framework to incorporate wavelet subbands in GMM to improve its performance; 2) an approach to incorporate variable number of clusters in the aforesaid framework; and 3) a generic platform to use any multiresolution decomposition based GMM for background suppression. Extensive experimentations on several video sequences are performed to verify the improvement in accuracy compared with conventional GMM as well as a number of state-of-the-arts approaches. Along with qualitative and quantitative analysis, justification on the use of multiresolution is provided for clarification.

The well-known finite mixture model (FMM) has been regarded as a useful tool for image segmentation application. However, the pixels in FMM are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account. These limitations make the FMM more sensitive to noise. In this brief, we propose a simple and effective method to make the traditional FMM more robust to noise with the help of a mean template. FMM can be considered a linear combination of prior and conditional probability from the expression of its mathematical formula. We calculate these probabilities with two mean templates: a weighted arithmetic mean template and a weighted geometric mean template. Thus, in our model, the prior probability (or conditional probability) of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood to incorporate the local spatial and intensity information for eliminating the noise. Finally, our algorithm is general enough and can be extended to any other FMM-based models to achieve super performance. Experimental results demonstrate the improved robustness and effectiveness of our approach.

This paper introduces a bilateral filtering based mixture model for image segmentation. The mixture model uses Markov Random Field (MRF) to incorporate spatial relationship among neighboring pixels into the Gaussian Mixture Model (GMM) in order to perform a segmentation that is robust against noise and other environmental factors. The bilateral filtering is used to smooth the posterior probability map as part of the MRF used. The advantage of the proposed model is its simplified structure so that the Expectation Maximization algorithm can be directly applied to the log-likelihood function to compute the optimum parameters of the mixture model. The method has been extensively tested on synthetic and natural images and compared with some of the state-of-the-arts algorithms currently available. The experimental results show that the proposed method is comparable to the other methods in terms of accuracy and quality and simpler in terms of implementation.

Finite mixture model with symmetric distribution has been widely used for many computer vision and pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and non-symmetric form. This study presents a new non-symmetric mixture model for image segmentation. The advantage of our method is that it is simple, easy to implement and intuitively appealing. In this paper, each label is modeled with multiple D-dimensional Student's-t distribution, which is heavily tailed and more robust than Gaussian distribution. Expectation maximization (EM) algorithm is adopted to estimate model parameters and to maximize the lower bound on the data log-likelihood from observations. Numerical experiments on various data types are conducted. The performance of the proposed model is compared to other mixture models, demonstrating the robustness, accuracy and effectiveness of our method.

Finite mixture model based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian, has recently received great attention for image segmentation. A new finite Student's-t mixture model (SMM) is proposed in this paper. Existing models do not explicitly incorporate the spatial relationships between pixels. First, our model exploits Dirichlet distribution and Dirichlet law to incorporate the local spatial constrains in an image. Secondly, we directly deal with the Student's-t distribution in order to estimate the model parameters, whereas, the Student's-t distributions in previous models are represented as an infinite mixture of scaled Gaussians that lead to an increase in complexity. Finally, instead of using expectation maximization (EM) algorithm, the proposed method adopts the gradient method to minimize the higher bound on the data negative log-likelihood and to optimize the parameters. The proposed model is successfully compared to the state-of-the-art finite mixture models. Numerical experiments are presented where the proposed model is tested on various simulated and real medical images.

In this paper, we present a new algorithm for pixel labeling and image segmentation based on the standard Gaussian mixture model (GMM). Unlike the standard GMM where pixels themselves are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account, the proposed method incorporates this spatial relationship into the standard GMM. Moreover, the proposed model requires fewer parameters compared with the models based on Markov random fields. In order to estimate model parameters from observations, instead of utilizing an expectation-maximization algorithm, we employ gradient method to minimize a higher bound on the data negative log-likelihood. The performance of the proposed model is compared with methods based on both standard GMM and Markov random fields, demonstrating the robustness, accuracy, and effectiveness of our method.

Standard gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for spatial dependences between image pixels. However, their main drawback is that they are computationally expensive to implement, and require large numbers of parameters. Based on these considerations, we propose an extension of the standard GMM for image segmentation, which utilizes a novel approach to incorporate the spatial relationships between neighboring pixels into the standard GMM. The proposed model is easy to implement and compared with MRF models, requires lesser number of parameters. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood, based on the gradient method. Experimental results obtained on noisy synthetic and real world grayscale images demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation, as compared to other methods based on standard GMM and MRF models.

In this paper, we present a efficient algorithm for real-time ellipse detection. Unlike Hough transform algorithm that is computationally intense and requires a higher dimensional parameter space, our proposed method reduces the computational complexity significantly, and accurately detects ellipses in realtime. We present a new method of detecting arc-segments from the image, based on the properties of ellipse. We then group the arc-segments into elliptical arcs in order to estimate the parameters of the ellipse, which are calculated using the least-square method. Our method has been tested and implemented on synthetic and real-world images containing both complete and incomplete ellipses. The performance is compared to existing ellipse detection algorithms, demonstrating the robustness, accuracy and effectiveness of our algorithm.

In this paper, we will present a new algorithm which is extended from the standard Gaussian mixture model to segment the noisy image based on the correlation among neighboring pixels. Firstly, we use the correlation between each centre pixel and its neighboring pixels in 3 times 3 window in building the prior probability, and this centre pixel is used to construct the conditional density function. Finally, to estimate the posterior probabilities of each pixel, instead of using expectation maximization algorithm as usual, we present a new maximum likelihood neural network (MaxNet) to optimize the parameters by using the error back propagation. Extensive experimental results illustrate the better performance compared to mixture model based on Markov random fields.

In this paper, we propose a new fuzzy rule-based system for application in image classification problem. Each rule in our proposed system can represent more than one class. While traditional fuzzy systems consider positive fuzzy rules only, in this paper, we focus on combining negative fuzzy rules with traditional positive ones leading to fuzzy inference systems. This new approach has been tested on image classification problem consisting of multiple images with excellent results.