一种基于马尔可夫随机场的运动目标检测算法

本文提出了一种基于马尔可夫随机场的运动目标检测算法.针对传统时间分割使用主观固定阁值的缺点,使用马尔可夫随机场模型对差分图像建模,并提出了一种新的模型阶次选择算法,以及一种可以加速收敛过程的随机场迭代算法.采用期望最大算法获取高斯分布参数并检测运动变化区域,利用形态学运算修正时间分割模板;空间分割部分提出了基于人眼视觉特征的改进分水岭算法,有效地解决了过分割问题;最后对时、空间分割结果进行信息融合处理,从而得到完整的运动目标.仿真实验结果证明了本文算法可以有效地分割视频运动目标.     

基于离散分数布朗随机场的水下图像目标检测

采用传统图像检测方法存在目标区域定位不准确、目标细节信息丢、目标形状变形等问题,本文提出一种基于离散分数布朗随机场模型的水下图像目标检测方法.该方法根据分形理论和水下图像的特点,以图像中每个像素点为中心取窗口,计算在该窗口内的分形维数均值,将该均值作为中心像素的分形特征,然后根据分形维数分布图确定分割阈值,从而实现对水下图像分割,并且通过将目标表面不同尺度下的灰度差分平均值进行归一化处理,减少了用于表示不同尺度下的平均绝对值灰度差分的数据,从而提高算法检测效率.实验结果表明,该方法对水下成像条件具有一定鲁棒性,是一种有效的水下图像目标检测方法.     

基于Markov随机场的区域跟踪算法

由于视频序列的对象跟踪相当于把图像帧分割成跟踪与非跟踪两个不重叠区域,为此,引入图像分割算法中的Markov随机场模型,提出了一种多目标模糊规划求取Markov标记场的最优估计来实现区域跟踪的算法。此算法为了克服传统离散Markov随机场运算速度慢的缺点,利用双随机矢量,建立连续的Markov标记场,同时提取区域视觉和运动信息的模糊特征,从而改善了算法的鲁棒性和运算复杂度。最终实验结果表明,此方法不仅跟踪效果好,而且还有运算速度快、抗干扰能力强等特点。     

一种基于DA-GMRF的无监督图像分割方法

提出一种基于间断自适应高斯马尔可夫随机场(DA-GMRF)模型的无监督图像分割方法.针对MRF模型中的过平滑问题,利用边缘信息构造能量函数,定义了一种DA-GMRF模型.利用灰度直方图势函数自动确定分类数及分割阈值,进行多阈值分割,得到DA-GMRF模型中标记场的初始化,用Metroplis采样器算法进行标记场的优化,实现了图像的无监督分割.实验结果表明了该方法的有效性.     

A New Inverse Algorithm for Tomographic Reconstruction of Damage Images Using Lamb Waves

Lamb wave tomography (LWT) is a potential and efficient technique for non-destructive tomographic reconstruction of damage images in structural components or materials. A new two-stage inverse algorithm with a small amount of scanning data for quickly reconstructing damage images in aluminum and CFRP laminated plates was proposed in this paper. Due to its high sensitivity to damages, the amplitude decrease of transmitted Lamb waves after travelling through the inspected region was employed as a key signal parameter related to the attenuation of Lamb waves in propagation routes. A through-thickness circular hole and a through-thickness elliptical hole in two aluminum plates, and an impact-induced invisible internal delamination in a CFRP laminated plate were used to validate the effectiveness and reliability of the proposed method. It was concluded that the present new algorithm was capable of reconstructing the images of the above mentioned various damages successfully with much less experimental data compared with those needed by some traditional techniques.     

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust. These systems are used to improve physicians’ diagnostic processes in terms of speed and accuracy. Using data-mining techniques, a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms. In this work, we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’ information and exposing them to cyber and network attacks. Solving the same problem with a different methodology, the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms. Moreover, our model is validated against the privacy requirements of the hospitals’ datasets and votes, and patients’ diagnosed symptoms.     

A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation

Tissue segmentation in magnetic resonance brain scans is the most critical task in different aspects of brain analysis. Because manual segmentation of brain magnetic resonance imaging (MRI) images is a time‐consuming and labor‐intensive procedure, automatic image segmentation is widely used for this purpose. As Markov Random Field (MRF) model provides a powerful tool for segmentation of images with a high level of artifacts, it has been considered as a superior method. But because of the high computational cost of MRF, it is not appropriate for online processing. This article has proposed a novel method based on a proper combination of MRF model and watershed algorithm in order to alleviate the MRF's drawbacks. Results illustrate that the proposed method has a good ability in MRI image segmentation, and also decreases the computational time effectively, which is a valuable improvement in the online applications. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 78–88, 2017     

A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and the ground truth (GT) was found by using the Jaccard and Dice similarity measuring method. According to the Jaccard similarity measuring method, WSCFMM showed an overall improvement of 4.72%, whereas the Dice similarity measuring method provided an overall improvement of 2.65% against the SCMM. Besides, WSCFMM significantly stabilized and reduced the execution time by showing an improvement of 83.71%. The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.     

大视场线阵CCD图像灰度均匀化

针对大视场线阵 CCD 图像不均匀现象,提出了一种基于图像像素灰度补偿的均匀化方法。首先分析并计算线形目标上不同坐标位置的微小面元在线阵CCD上产生的辐射照度,并由此推导线阵CCD面元上不同像素灰度值与中心像素灰度值之间的关系,进而建立大视场线阵CCD像面上像素的灰度补偿模型,实现大视场线阵CCD图像快速均匀化的目的。对大量线阵 CCD 采集的高速路面图像进行了处理,结果表明我们提出的方法具有较强的可行性、鲁棒性和实用性。     

A note on decoupling of local and global behaviours for the Dagum Random Field

Self-affinity versus decoupling: this dichotomy represents a breakthrough with respect to the previous literature, that has grown under the dogma of self-affinity. The word decoupling refers to those correlation functions allowing to treat independently the Hausdorff–Besicovitch dimension and Hurst effect parameters. The former is a roughness measure associated to profiles or surfaces. The latter reflects possible persistent or antipersistent behaviours of the associated random process or random field. Thus, the decoupling philosophy opens new avenues for the analysis and interpretation of local and global properties of random fields. In this paper, we introduce a new class of isotropic correlation functions, called Dagum, show its permissibility on any n-dimensional space, and analyse its attitudes with respect to decoupling. Interesting aspects arise from an intensive simulation study, conducted in one and two dimensions. In particular, it seems that the decoupling attitude may depend on the space dimension.     

Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility

Service accessibility is defined as the access of a community to the nearby site locations in a service network consisting of multiple geographically distributed service sites. Leveraging new statistical methods, this article estimates and classifies service accessibility patterns varying over a large geographic area (Georgia) and over a period of 16 years. The focus of this study is on financial services but it generally applies to any other service operation. To this end, we introduce a model-based method for clustering random time-varying functions that are spatially interdependent. The underlying clustering model is nonparametric with spatially correlated errors. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across functions corresponding to nearby spatial locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership as shown in a simulation study. Supplementary materials including the estimation algorithm, additional maps of the data, and the C++ computer programs for analyzing the data in our case study are available online.     

灰色马尔可夫链在道路交通事故预测中的应用

将灰色系统理论与马尔可夫链相结合,建立道路交通事故灰色马尔可夫链预测模型研究证明．这种模型的预测精度高于GM（1,1）模型,可用于道路交通事故预测。     

A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images. The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space, cost savings. However, the openness of cloud brings challenges for image data security. In this paper, we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment, which takes the weights of participants (i.e., cloud service providers) into consideration. An extended Mignotte sequence is constructed according to the weights of participants, and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images. Then we store the shadows in every cloud service provider, respectively. At last, we restore the remote sensing image based on the Chinese Remainder Theorem. Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud. The experiment also shows that no matter weight values, each service providers only needs to save one share, which simplifies the management and usage, it also reduces the transmission of secret information, strengthens the security and practicality of this scheme.     

AGFA CR系统影像伪影的探讨

通过分析AGFA CR系统各种伪影的特点,从扫描器的机械结构、工作原理、IP板的构造、后处理工作站软件应用等方面阐述CR伪影的产生原因。探讨减少CR伪影的解决对策。     

A Robust Zero-Watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain

Remote medical diagnosis can be realized by using the Internet, but when transmitting medical images of patients through the Internet, personal information of patients may be leaked. Aim at the security of medical information system and the protection of medical images, a novel robust zero-watermarking based on SIFT-DCT (Scale Invariant Feature Transform-Discrete Cosine Transform) for medical images in the encrypted domain is proposed. Firstly, the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images. Then, the SIFT-DCT is used to extract the feature sequences of encrypted medical images. Next, zero-watermarking technology is used to ensure that the region of interest of medical images are not changed. Finally, the robust of the algorithm is evaluated by the correlation coefficient between the original watermark and the attacked watermark. A series of attack experiments are carried out on this method, and the results show that the algorithm is not only secure, but also robust to both traditional and geometric attacks, especially in clipping attacks.     

Median Filtering Forensics Scheme for Color Images Based on Quaternion Magnitude-Phase CNN

In the paper, a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images. Compared with conventional convolutional neural network, color images can be processed in a holistic manner in the proposed scheme, which makes full use of the correlation between RGB channels. And due to the use of convolutional neural network, it can effectively avoid the one-sidedness of artificial features. Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection.     

一种基于平均步态能量图的身份识别算法

提出一种基于步态能量图（GEI）的嵌入式隐马尔可夫模型（e-HMM）身份识别方法。首先通过预处理提取出运动人体的侧面轮廓,根据步态下肢的摆动距离统计出步态周期,得到平均步态能量图。对能量图的各区域进行分析,利用二维离散余弦变换（2D-DCT）将能量图观测块转化为观测向量,实现嵌入式隐马尔可夫模型的训练和身份识别。最后在USF和CASIA步态数据库上对所提出的算法进行实验。实验表明该方法具有较好的识别性能,是一种有效的步态识别方法。     

虚拟环境中最佳观察视场角的实验研究

虚拟环境视场角的设置对其能否真实地模拟现实场景和被试者对其中目标判断 (即大小和角度判断 )有很大的影响 ,为此文章进行了最佳的观察视场角的实验研究。首先选定被试并在基于PC平台的虚拟现实工效学实验系统上进行一定人次的预实验 ,确定虚拟环境中进行实验时观察任务的差别和视场角的影响等级 ,然后 4 5名被试头戴头盔进行单因素、4水平的完全随机实验。方差分析表明 ,虚拟环境中视场角设置对人的判断工效具有显著性影响 ,同时 ,当视场角为 6 0°时 ,被试对其中目标判断的准确性最大。因此 ,利用VR技术进行工效设计、操作训练或其他任务的研究时 ,应考虑视场角的作用 ,它的值宜取 6 0° ,这时对人的判读工效较为有利     

一种多点随机振动试验控制的新方法研究

为适应多振动台试验控制的需求，提出了一种新的多点随机振动实验控制方法。该方法通过构造一组有限冲击响应(FIR)滤波器对独立的白噪声序列进行线性滤波耦合而直接生成驱动信号，在闭环控制中实时修正驱动信号，使得最终响应谱满足参考谱要求。由于抛弃了传统的驱动信号时域随机化过程，这使得该方法具有较强的稳定性和适应性，而且控制回路时间缩短，提高了控制系统的实时性。文中提供了一模拟算例，显示了该方法的有效性。     

Four‐neighborhood clique kernel: A general framework for Bayesian and variational techniques of noise reduction in magnetic resonance images of the brain

Several algorithms have been proposed in the literature for image denoising but none exhibit optimal performance for all range and types of noise and for all image acquisition modes. We describe a new general framework, built from four‐neighborhood clique system, for denoising medical images. The kernel quantifies smoothness energy of spatially continuous anatomical structures. Scalar and vector valued quantification of smoothness energy configures images for Bayesian and variational denoising modes, respectively. Within variational mode, the choice of norm adapts images for either total variation or Tikhonov technique. Our proposal has three significant contributions. First, it demonstrates that the four‐neighborhood clique kernel is a basic filter, in same class as Gaussian and wavelet filters, from which state‐of‐the‐art denoising algorithms are derived. Second, we formulate theoretical analysis, which connects and integrates Bayesian and variational techniques into a two‐layer structured denoising system. Third, our proposal reveals that the first layer of the new denoising system is a hitherto unknown form of Markov random field model referred to as single‐layer Markov random field (SLMRF). The new model denoises a specific type of medical image by minimizing energy subject to knowledge of mathematical model that describes relationship between the image smoothness energy and noise level but without reference to a classical prior model. SLMRF was applied to and evaluated on two real brain magnetic resonance imaging datasets acquired with different protocols. Comparative performance evaluation shows that our proposal is comparable to state‐of‐the‐art algorithms. SLMRF is simple and computationally efficient because it does not incorporate a regularization parameter. Furthermore, it preserves edges and its output is devoid of blurring and ringing artifacts associated with Gaussian‐based and wavelet‐based algorithms. The denoising system is potentially applicable to speckle reduction in ultrasound images and extendable to three‐layer structure that account for texture features in medical images. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 224–238, 2014