Adaptive Segmentation Model for Images with Intensity Inhomogeneity based on Local Neighborhood Contrast

Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing, especially in medical image processing and analysis. Some local region-based models work well on handling intensity inhomogeneity, but they are always sensitive to contour initialization and high noise. In this paper, we present an adaptive segmentation model for images with intensity inhomogeneity in the form of partial differential equation. Firstly, a global intensity fitting term and a local intensity fitting term are constructed by employing the global and local image information, respectively. Secondly, a tradeoff function is defined to adjust adaptively the weight between two fitting terms, which is based on the neighborhood contrast of image pixel. Finally, a weighted regularization term related to local entropy is used to ensure the smoothness of evolution curve. Meanwhile, a distance regularization term is added for stable level set evolution. Experimental results show that the proposed model without initial contour can segment inhomogeneous images stably and effectively, which thereby avoiding the influence of contour initialization on segmentation results. Besides, the proposed model works better on noise images comparing with two relevant segmentation models.     

Split Bregman method for minimization of improved active contour model combining local and global information dynamically

This paper presents an improved active contour model by combining the Chan–Vese model, the region-scalable fitting energy model, the globally convex segmentation method and the split Bregman method. A weight function that varies with the location of a given image is used to control the influence of the local and global information dynamically. We first present our model in a 2-phase level set formulation and then extend it to a multi-phase formulation. By taking the local and global information into consideration together, our model can segment more general images, especially images with intensity inhomogeneity. Our model has been applied to synthetic and real images with promising results. Numerical results show the advantages of our model compared with other models. The accuracy and efficiency are demonstrated by the numerical results. Besides, our model is robust in the presence of noise.     

A fractional order derivative based active contour model for inhomogeneous image segmentation

Segmenting intensity inhomogeneous images is a challenging task for both local and global methods. Some hybrid methods have great advantages over the traditional methods in inhomogeneous image segmentation. In this paper, a new hybrid method is presented, which incorporates image gradient, local environment and global information into a framework, called adaptive-weighting active contour model. The energy or level set functions in the framework mainly include two parts: a global term and local term. The global term aims to enhance the image contrast, and it can also accelerate the convergence rate when minimizing the energy function. The local term integrates fractional order differentiation, fractional order gradient magnitude, and difference image information into the well-known local Chan–Vese model, which has been shown to be effective and efficient in modeling the local information. The local term can also enhance low frequency information and improve the inhomogeneous image segmentation. An adaptive weighting strategy is proposed to balance the actions of the global and local terms automatically. When minimizing the level set functions, regularization can be imposed by applying Gaussian filtering to ensure smoothness in the evolution process. In addition, a corresponding stopping criterion is proposed to ensure the evolving curve automatically stops on true boundaries of objects. Dice similarity coefficient is employed as the comparative quantitative measures for the segmented results. Experiments on synthetic images as well as real images are performed to demonstrate the segmentation accuracy and computational efficiency of the presented hybrid method.     

A modified candecomp algorithm for fitting the latent class model: Implementation and evaluation

In this paper an implementation is discussed of a modified CANDECOMP algorithm for fitting Lazarsfeld's latent class model. The CANDECOMP algorithm is modified such that the resulting parameter estimates are non-negative and ‘best asymptotically normal’. In order to achieve this, the modified CANDECOMP algorithm minimizes a weighted least squares function instead of an unweighted least squares function as the traditional CANDECOMP algorithm does. To evaluate the new procedure, the modified CANDECOMP procedure with different weighting schemes is compared on five published data sets with the widely-used iterative proportional fitting procedure for obtaining maximum likelihood estimates of the parameters in the latent class model. It is found that, with appropriate weights, the modified CANDECOMP algorithm yields solutions that are nearly identical with those obtained by means of the maximum likelihood procedure. While the modified CANDECOMP algorithm tends to be computationally more intensive than the maximum likelihood method, it is very flexible in that it easily allows one to try out different weighting schemes.     

Recursive parameter identification for fermentation processes with the multiple model technique

This paper considers parameter identification problems for a fermentation process. Since the fermentation process is nonlinear, it is difficult to use a single-model for describing such a process and thus we use the multiple model technique to study the identification methods. The basic idea is to establish the model of the fermentation process at each operation point by means of the least squares principle, to obtain multiple models with different points, and then use the weighting functions or interpolation methods to compute the total model or the global model. Finally, a numerical example is provided to test the effectiveness of the proposed algorithm.     

周期数据时段划分的双截断高斯混合模型及其EM算法

有限混合模型是多模态数据拟合和聚类的有力工具,本文针对具有多模态的周期数据提出了双截断高斯混合糢型,并推导出相应的EM算法,再通过BIC准則确定混合成分个数,该方法的优点是可以将相邻周期上距离较近的数据聚为一类.模拟研究显示,在具体参数设置下,EM算法和BIC准则是相合的。最后,该方法应用于车流量数据的时段划分,将一天划分为具有显著特征的6个时段,有助于交通部门采取相应策略,为优化交通灯信号配时提供参考依据.     

中国碳排放变化的因素分解与减排路径研究

能源及其引致的碳排放等相关问题已经成为影响人类社会发展全局和全球政治经济格局的重大战略问题.中国是世界上最大的发展中国家,面临着更严峻的能源挑战.节约能源、大幅度改善能源效率是我国应对能源和气侯变化挑战的一条极其重要且有效途径.本文综合考量了能源结构、能源强度、能源效率及经济增长等4个因素对碳排放的影响,基于因素分解模型,应用LMDI分解方法对中国一次能源利用的CO2排放及碳排放强度变化进行了研究,研究发现二氧化碳排放增加主要是由于经济增长、人口规模扩大引起的.在此基础上提出了碳减排的政策建议.     

New normalized nonlocal hybrid level set method for image segmentation

This article introduces a new normalized nonlocal hybrid level set method for image segmentation. Due to intensity overlapping, blurred edges with complex backgrounds, simple intensity and texture information, such kind of image segmentation is still a challenging task. The proposed method uses both the region and boundary information to achieve accurate segmentation results. The region information can help to identify rough region of interest and prevent the boundary leakage problem. It makes use of normalized nonlocal comparisons between pairs of patches in each region, and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation. The boundary information can help to detect the precise location of the target object, it makes use of the geodesic active contour model to obtain the target boundary. The corresponding variational segmentation problem is implemented by a level set formulation. We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function. At last, experimental results on synthetic images and real images are shown in the paper with promising results.     

一种新的基于SVM参数优化的算法

针对单纯使用遗传算法处理大规模数据需要时间长和对计算机的内存等硬件要求较高的问题,将神经网络嵌入到遗传算法中构造出混合智能遗传算法用于SVM核函数的参数优化,数值试验结果表明该算法对SVM核参数优化是可行的、有效的,并能得到较好的SVM核参数组合和具有较高的分类准确率及较好的泛化能力.     

Maximum consistency method for data fitting under interval uncertainty

The work is devoted to application of global optimization in data fitting problem under interval uncertainty. Parameters of the linear function that best fits intervally defined data are taken as the maximum point for a special (“recognizing”) functional which is shown to characterize consistency between the data and parameters. The new data fitting technique is therefore called “maximum consistency method”. We investigate properties of the recognizing functional and present interpretation of the parameter estimates produced by the maximum consistency method.     

半参数回归模型中随机加权M估计的强逼近

用随机加权法给出了半参数回归模型中参数的随机加权M估计，在一般的条件下证明了用随机加权统计量的分布逼近原估计量误差的分布的强有效性，并给出了M估计的最优强收敛速度。     

Identification of mechanical properties of arteries with certification of global optimality

In this study, we consider identification of parameters in a non-linear continuum-mechanical model of arteries by fitting the models response to clinical data. The fitting of the model is formulated as a constrained non-linear, non-convex least-squares minimization problem. The model parameters are directly related to the underlying physiology of arteries, and correctly identified they can be of great clinical value. The non-convexity of the minimization problem implies that incorrect parameter values, corresponding to local minima or stationary points may be found, however. Therefore, we investigate the feasibility of using a branch-and-bound algorithm to identify the parameters to global optimality. The algorithm is tested on three clinical data sets, in each case using four increasingly larger regions around a candidate global solution in the parameter space. In all cases, the candidate global solution is found already in the initialization phase when solving the original non-convex minimization problem from multiple starting points, and the remaining time is spent on increasing the lower bound on the optimal value. Although the branch-and-bound algorithm is parallelized, the overall procedure is in general very time-consuming.

     

Richards模型参数估计及其模型应用

在非线性模型中,Richards模型是一个含有四参数的增长曲线模型,该模型对数据的拟合有较强的适应性,应用较为广泛.但其参数的估计较为复杂,给出简便易行的三种方法,实例应用表明拟合效果很好.     

On weighting two criteria with a parameter in combinatorial optimization problems

Two criteria in a combinatorial problem are often combined in a weighted sum objective using a weighting parameter between 0 and 1. For special problem types, e.g., when one of the criteria is a bottleneck value, efficient algorithms are known that solve for a given value of the weighting parameter.     

Ellipsoidal BGK model for polyatomic molecules near Maxwellians: A dichotomy in the dissipation estimate

We consider the global existence and asymptotic behavior of classical solutions to the ellipsoidal BGK model for polyatomic molecules when the initial data starts sufficiently close to a global polyatomic Maxwellian. We observe that the linearized relaxation operator is decomposed into a truly polyatomic part and an essentially monatomic part, leading to a dichotomy in the dissipative property in the sense that the degeneracy of the dissipation shows an abrupt jump as the relaxation parameter θ reaches zero. Accordingly, we employ two different sets of micro–macro system to derive the full coercivity and close the energy estimate.     

Global optimization by random perturbation of the gradient method with a fixed parameter

The paper deals with the global minimization of a differentiable cost function mapping a ball of a finite dimensional Euclidean space into an interval of real numbers. It is established that a suitable random perturbation of the gradient method with a fixed parameter generates a bounded minimizing sequence and leads to a global minimum: the perturbation avoids convergence to local minima. The stated results suggest an algorithm for the numerical approximation of global minima: experiments are performed for the problem of fitting a sum of exponentials to discrete data and to a nonlinear system involving about 5000 variables. The effect of the random perturbation is examined by comparison with the purely deterministic gradient method.     

Robustness analysis for parameter matrices of global exponential stable stochastic time varying delay systems

In this paper, we analyze the robustness of global exponential stable stochastic delayed systems subject to the uncertainty in parameter matrices. Given a globally exponentially stable systems, the problem to be addressed here is how much uncertainty in parameter matrices the systems can withstand to be globally exponentially stable. The upper bounds of the parameter uncertainty intensity are characterized by using transcendental equation for the systems to sustain global exponential stability. Moreover, we prove theoretically that, the globally exponentially stable systems, if additive uncertainties in parameter matrices are smaller than the upper bounds arrived at here, then the perturbed systems are guaranteed to also be globally exponentially stable. Two numerical examples are provided here to illustrate the theoretical results.     

The Freedericksz Transition and the Asymptotic Behavior in Nematic Liquid Crystals

We consider the stability of a specific nematic liquid crystal configuration under an applied magnetic field. We show that for some specific configuration there exist two critical values H_n and H_{sh} of applied magnetic field. When the intensity of the magnetic field is smaller than H_n, the configuration of the energy is only global minimizer, when the intensity is between H_n and H_{sh}, the configuration is not global minimizer, but is weakly stable, and when the intensity is larger than H_{sh}, the configuration is instable. Moreover, we also examine the asymptotic behavior of the global minimizer as the intensity tends to the infinity.     

The Darboux Transformation and Exactly Solvable Cosmological Models

We present a simple and effective method for constructing exactly solvable cosmological models containing inflation with exit. This method does not involve any parameter fitting. We discuss the problems arising with solutions that violate the weak energy condition.