. In this paper, we study the complete consistency for the estimator of nonparametric regression models based on extended negatively dependent random errors by using the exponen-tial inequalities and the truncation method. In particular, if E | X | 1 + p < ∞ for some p > 1, then the result also holds, which improves the corresponding one in the literature. As an application, the complete consistency for the nearest neighbor estimator is obtained. Finally, the simulation study is provided to verify the validity of the theoretical result.

A novel integrated two-stage approach is proposed for image segmentation, where the edge, global and local region information of images are in turn incorporated to define the intensity fitting energy. In the first stage, the Chan-Vese model flexibly assimilates the edge indicator function in the beginning, and then the Laplace operator is introduced to regularize the level set function when minimizing the energy functional. As an edge-based and global region-based active contour, it can be inclined to rapidly produce a coarse segmentation result. In the second stage, we further segment the image by absorbing the local region fitting energy, where its initialization is acquired by the final active contour of the first stage. In addition, we present a generalized level set regularization term, which efficiently eliminates the periodically re-initialization procedure of traditional level set methods and maintains the corresponding signed distance property. Compared with the first stage, the local object details are accurately segmented in the second stage, which can acquire an accurate segmentation result. Qualitative and quantitative experimental results demonstrate the accuracy, robustness and efficiency of our approach with applications to some synthetical and real-world images.

针对灰度分布不均匀的图像特征,提出一种基于局部和全局高斯分布拟合能量的自适应权重参数选择方法。基于图像的局部和全局区域信息,以高斯分布作为拟合函数建立能量泛函。基于水平集方法,随着活动轮廓的演化,局部和全局区域信息在能量泛函中的权重会相应地变化,有利于提高图像分割的质量和效率。数值实验验证了该方法的有效性。

The moving detection and understanding are two important parts of intelligent monitoring. Firstly, the initial background was created by mean algorithm, then the Self Adapted Background Subtraction Algorithm was used to detect the moving object, and the Gaussian Hermite Moment, the centroid and area feature were extracted, and the tracking algorithm of moving object was developed with the help of these features. The classification of moving object was realized with the help of the feature and the Adaptive Resonance Theory (ART). Finally, we can understand the behavior of moving objects based on the warning line, understand the moving objects based on ART, and understand the behavior of moving objects based on trajectories. The experiment shows that the algorithm was effective.

Based on popular active contours, this paper proposes a novel two-stage image segmentation method, which incorporates the global and local image region fitting energies. In the first stage, according to the global region active contour, we preliminarily segment the image by globally using the Gaussian distribution, which can rapidly get a coarse segmentation result. Subsequently, by employing a window function, we further segment the image by using the local region active contour, where we use the final active contour of the first stage as the initialization. Compared with the first stage, the local object details are accurately segmented in the second stage, which can be considered as an accurate segmentation result. Due to the suitable initialization from the first stage, the second stage works well in accurately segmenting the image, especially in local details. To regularize the level set function, we introduce a Laplace operator, which efficiently eliminates the expensive re-initialization process of traditional level set methods. Compared with the state-of-the-art methods, experiment results demonstrate the effectiveness and performance of the proposed method with applications to synthetical and real-world images, which usually contain noise, blurry boundaries, and intensity inhomogeneities.

In this article, we establish the complete moment convergence of a moving-average process generated by a class of random variables satisfying the Rosenthal-type maximal inequality and the week mean dominating condition. Onthe one hand, we give the correct proof for the case p = 1 in Ko (2015); on the other hand, we also consider the case alpha p = 1 whichwas not considered in Ko (2015). The results obtained in this article generalize some corresponding ones for some dependent sequences.

In this paper, we propose an adaptive weighting parameter selection method for improved integrated active contour model. By employing a weighting parameter, we first integrate the local gradient information into the Chan–Vese model. In order to regularize the level set function, we present a new restraint term to improve the integrated active contour model. Then we propose a novel method to dynamically select the weighting parameter, which can adaptively adjust the weight between the local gradient information and the global region information. The proposed method is flexible to improve the segmentation quality and experiment results demonstrate its effectiveness.

This paper presents a variant of the level set function based on region-scalable fitting (RSF) model for segmenting a given image into different parts. In consideration of the image local characteristics, the RSF model can efficiently and effectively segment images with intensity inhomogeneity. Instead of utilizing n level set functions to define up to 2n phases in the RSF model, our method presents a piecewise constant level set formulation for image segmentation and each phase is represented by a unique constant value. In addition, our model avoids different segmentation results caused by different initializations. The energy functional of our method is locally differentiable and convex because we do not use the nondifferentiable Heaviside and Delta functions. Comparative experiment results demonstrate that our method is much more computationally efficient. Moreover, our algorithm is robust against destructive noise.

In this paper, by employing the cosine function to express the so-called data fitting term in traditional active contour models, we propose an active contour model with the global cosine fitting energy for segmenting synthetic and real-world images. After that, in order to segment the image with intensity inhomogeneity, we extend the proposed global model to the local cosine fitting energy. In addition, we introduce level set regularization terms into the proposed models to avoid the expensive computational cost which is usually caused by the reinitialization of the evolving level set function. Experimental results indicate that the proposed models are accurate and effective when applied to segment different types of images. Moreover, our models are more efficient and robust for segmenting the images with strong noise and clutter than the Chan-Vese model and the local binary fitting model.

Active contour model is popularly and widely used in the field of image segmentation, which is based on superior theoretical properties and efficient numerical methods. Nevertheless, one of the prominent disadvantages of this kind of model is the existence of local minima in its functional energy. In this paper, we propose a novel global minimization hybrid active contour model. This model effectively integrates the edge information, the local region information and the global region information of the image, which is relatively sufficient to extract the object boundaries. Furthermore, we introduce an efficient and fast numerical approach to globally minimize the proposed model, which is through a dual formulation of the minimization problem and easy to implement. The proposed model is robust enough to the initial condition and does not need to initialize the contour in a distance function and re-initialize it periodically during the evolution process. Specially, we applied the proposed model to segment oil spill images, in which there usually exist the noise, blurry boundaries, and intensity inhomogeneity. Compared with the state-of-the-art models, experiment results demonstrate the performance and effectiveness of the proposed model with applications to synthetical and real images, especially for oil spill images.

In this paper, we propose an active contour model and its corresponding algorithms with detailed implementation for image segmentation. In the proposed model, the local and global region fitting energies are described by the combination of the local and global Gaussian distributions with different means and variances, respectively. In this combination, we increase a weighting coefficient by which we can adjust the ratio between the local and global region fitting energies. Then we present an algorithm for implementing the proposed model directly. Considering that, in practice, the selection of the weighting coefficient is troublesome, we present a modified algorithm in order to overcome this problem and increase the flexibility. By adaptively updating the weighting coefficient and the time step with the contour evolution, this algorithm is less sensitive to the initialization of the contour and can speed up the convergence rate. Besides, it is robust to the noise and can be used to extract the desired objects. Experiment results demonstrate that the proposed model and its algorithms are effective with application to both the synthetic and real-world images.

•A weighting parameter is introduced to integrate the local and global intensity fitting energy.•An adaptive weighting parameter estimation method is proposed.•Experimental results demonstrate the performance of the proposed method.

In this paper, an efficient active contour with Gaussian distribution fitting energy is proposed for image segmentation. We introduce the Gaussian distribution to construct the fitting energy, which drives the active contour toward the object boundaries. In order to protect the smoothness property and the stability, a reaction diffusion approach is borrowed to design a two-stage scheme, which eliminates the procedure of re-initialization of traditional level set methods and improves the segmentation quality effectively.

In this paper, we propose an efficient operator splitting method for local region Chan-Vese (C-V) model for image segmentation. Different from the C-V model, we employ the window function and absorb the local characteristics of the image for improving the C-V model, which we called the local C-V model. The local C-V model can deal with the problem of intensity inhomogeneity which widely exists in the real-world images. By employing a Laplacian operator, we present an operator splitting method to update the level set function. Firstly, we solve the proposed model for evolving the level set function, which drives the active contour to move toward the object boundaries. Secondly, we introduce the Laplacian operator to act on the level set function as a diffusion term, which could efficiently ensure the smoothness and stability and eliminate the complex process of re-initialization. Besides, we increase a new constraint term which avoids updating the level set function seriously. Furthermore, we present an extension for vector-valued images. Experiment results show that our method is competitive with application to synthetic and real-world images.

Biological computations like Electrocardiological modeling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of Electrocardiograms (ECGs). We realized the parallel computation for computer simulation of ECGs on a CPU-GPU cluster using a hybrid parallel algorithm with the parallel program development tools-MPI, OpenMP, and CUDA. Furthermore, we proposed a load-prediction static scheduling and load-prediction dynamic scheduling to achieve efficient process-level and thread-level scheduling, respectively. Compared with traditional static and dynamic scheduling, our scheduling schemes are more efficient. In our research, we achieved a speedup of 55.1 using four PCs for the computer simulation of ECGs. This study demonstrates that the cluster can provide a cheap and efficient environment for parallel computations in biological modeling and simulation studies.

Two series of exchange coupled composite films with and without Ag interlayer, L1(0)-[FePt-Ag]/[Co/Ni](N) (FC-Ag-N) and FePt/[Co/Ni](N) (FC-N), were prepared and well studied. Coercivities of both series decrease with the increase of the soft [Co/Ni](N) thickness. However, well-isolated sphere grains are observed in FC-Ag-N samples, instead of a ledge-type structure in FC-N samples. The average grain size is almost constant for FC-Ag-N films, regardless of the thickness of Co/Ni multilayer. It indicates that Ag effectively suppresses the grain coarseness during the deposition of [Co/Ni](N). (C) 2013 AIP Publishing LLC.

In this paper, we propose a region-based model for the object and background extraction with application to the image with thick or complex boundary. Based on region information of the image, we employ two curves to extract the object and background, respectively, regardless of the boundary. The first curve is used to extract the object. Correspondingly, the second curve is used to extract the background. By employing two level set functions to represent the two curves, we propose a new region-based energy functional. In the proposed model, a distance constraint term is incorporated, which effectively avoid that the two level set functions too away from each other and keep their similar shapes well. Besides, we present a penalty term to maintain the accurate computation and stability evolution. Experiment results demonstrate the desirable performance of the proposed model with application to synthetic and real-world images.

This paper proposes a new variational method with a non-convex regularization term which is introduced to restore high quality image. Non-convex regularization has advantages over convex regularization such as total variation (TV) for image segmentation. In practical, the used of the non-convex regularization is limited by the difficulty of the minimization. Through the variation splitting technology, we develop a new fast minimization algorithm to solve the non-convex problem for image segmentation. The new algorithm has higher efficiency and more robust to the choice of parameters. Experimental results illustrate the performance improvements by using our method.

While measurement of QT prolongation in electrocardiogram (ECG) is important to clinical applications, but it is difficult to quantitatively evaluate the performance of QT measurement algorithms using clinical ECGs because the true values of the QT changes are usually unknown. In this paper, we report a comparative study to evaluate the performance of the six algorithms for QT interval prolongation measurement with computer-simulated ECGs based on a whole-heart model. Artificial zero-mean Gaussian noise with variable signal-to-noise ratios (SNR) was added to the simulated ECGs for quantitative evaluation of these algorithms with variations thresholds. The study showed that, the measurement accuracies of these six methods augment with the increase of the SNR of simulation data, while the variations of threshold of these methods have little influence on the measurement accuracy of QT interval variation. Through evaluating the QT interval prolongation values measured from the same simulated ECGs contaminated with noises, the best performance was obtained from the continuous wavelet transform based method with the mean error less than 1 ms even when the SNR is as low as 0 dB. Our study suggests that the continuous wavelet transform based method may outperform the traditional methods in estimating QT interval prolongation for ECGs with strong noise as evaluated with artificial ECGs.

In parallelizing compilers on distributed memory systems, distributions of irregular sized array blocks are provided for load balancing and irregular problems. The irregular data redistribution is different from the regular block-cyclic redistribution. This paper is devoted to scheduling message for irregular data redistribution that attempt to obtain suboptimal solutions while satisfying the minimal communication costs condition and the minimal step condition. Based on the list scheduling, an efficient algorithm is developed and its experimental results are compared with previous algorithms. The improved list algorithm provides more chance for conflict messages in its relocation phase, since it allocates conflict messages through methods used in a divide-and-conquer algorithm and a relocation algorithm proposed previously. The method of selecting the smallest relocation cost guarantees that the improved list algorithm is more efficient than the other two in average.