基于Scenario的MAS环境建模方法

对多Agent系统（MAS）的环境建模将有助于促进复杂MAS的分析和设计。本文提出了一种基于Scenario的MAS环境建模方法,该方法通过描述MAS的Scenario导出和分析MAS的环境,并采用高层的组织抽象思想对MAS的环境进行建模。本文通过一个电子商务系统的案例分析说明如何利用该方法指导MAS的开发。     

一种新的基于复合弹簧网络的多Agent系统分布式问题求解方法

当多Agent系统（MAS）中Agent之间存在多种复杂的随机的社会交互行为时，当各Agent表现出不同程度的自治性和理性时，难以用现有的方法描述和求解MAS问题，即使对仅仅存在竞争和合作这两种社会交互行为，并且不考虑Agent之间自治程度的本质性差异时，现有的基于结盟的MAS问题求解算法也具有极高的计算复杂性，该文提出一种新的复合弹簧网络模型和方法，利用分布式弹性动力学方程，将MAS分布式问题求解过程转变对应的复合弹簧网络形变过程，这种模型和方法能够处理各种社会交互行为以及Agent不同程度的自治性，分析和仿真实验表明，在计算复杂性和适用性等许多方面，该文的分布并行算法优于文献[7,8]的Shehory-Kraus算法。     

基于MAS的复杂系统分布式求解策略与推理研究

针对复杂的连续生产线系统监测与故障诊断的动态、分布、实时和不确定特性，将多Agent及相关智能技术引入复杂故障诊断领域，提出了一种嵌套式基于消息传递的多Agent组织模型，分析了基于MAS的分布式智能故障诊断方法和过程；研究了模型系统的动态适应性和稳定性；设计了实时诊断Agent工作状态的表达机制；讨论了Agent间的协调协作机制及融合方法；给出了多Agent诊断系统诊断决策的集成描述结构；提出了任务分解与分配调度算法，以充分发挥Agent的社会性和基于场景的特点，使得在复杂系统的问题求解中能提供更为可靠的诊断结果，而且还能节约资源提高诊断效率．将其应用于某安全监控系统中，取得了与专家相似的诊断结果，克服了以往监控诊断系统存在的弊端，提高了企业的安全运行效率．与传统诊断方法相比，体现了Agent技术在复杂分布式问题求解领域的特有优势和良好前景．     

冲突环境下中立Agent群体趋向分析与预测

分布式智能系统基本研究领域之一就是多Agent系统．在MAS协作过程中冲突难以避免，在实际应用的许多情况下，冲突都是在群体层面展开，同时冲突双方都需要特别关注持中立态度Agent的未来发展导向，这在双方势均力敌情况下尤为重要．因此，根据已有信息，建立基于中立Agent群体的智能分析与计算模型，对中立群体可能的趋向进行必要评估和预测，在冲突研究与应用当中具有基本意义．通过对MAS中Agent相互关系的形式描述，引入中立Agent群体模型，建立了对中立Agent未来状态进行智能分析和预测的计算模式，并设计了相应算法和实验．理论分析和实验结果表明了所建立模型的合理性与有效性。     

基于MAS的网络坦克作战系统体系结构分析

网络坦克作战系统是一个复杂系统,对这一系统的研究实质上是一个复杂问题的求解过程.Agent技术为此提供了模块化及抽象化的问题求解方法.将网络坦克作战系统从功能结构的角度进行了模块化分割,提出了一种基于多Agent系统(MAS)的体系结构,将系统中的协作Agent分为5类:指挥决策Agent、局域协调控制Agent、分队级协调控制Agent,系统重构Agent和评价Agent,对这种体系结构进行了分析与研究.     

MAS系统的问题求解能力分析

本文用状态空间搜索模型分析了多Agent系统(MAS)的问题求解能力，认为MAS系统中Agent之间知识的组合应用和对问题搜索方向的交互和决策是影响MAS系统问题求解能力的主要原因，在状态空间搜索模型下可以将Agent间知识的组合应用表达为不同Agent的搜索路径的组合，而Agent对搜索方向的判断是基于启发式信息做出的，从而为形式化分析MAS系统的性能建立了通用的模型．本文以A*算法为例探讨了可采纳算法下多Agent合作求解效果与Agent的知识和启发信息之间的关系，指出只有在一定条件下MAS系统才会获得更好的解题能力．本文还对非可采纳算法下MAS系统性能分析方法提出了初步看法．     

基于归一化割的自适应多阈值图像分割方法

基于图谱理论的图像闽值分割方法是一种基于全局的单阈值分割方法,其对复杂图像的分割效果不明显,而且其采用的权值计算公式中含有两个需手动调整的参数,这些都限制了该算法的通用性.据此提出基于归一化割的自适应多阈值图像分割方法,它通过几个阈值来分割复杂图像,并且采用了一个自适应权值计算公式,提高了该方法的通用性.大量的实验结果表明该方法具有很好的分割效果,可以保留更多的图像细节.     

基于分量直方图的移动机器人视觉图像自适应分割

提出一种基于分量直方图的自适应分割方法．首先对图像的3个分量统计直方图进行自适应分割，确定出各分量的分类数目及类的取值范围；然后，对分割类进行分量间组合，获得原图像中主要的几种颜色；最后以这些颜色作为聚类中心，按照颜色相似性准则对图像进行聚类分割．在移动机器人CASIA 1上进行的实验证明：该算法实现了图像主要区域的自适应分割与提取，并且能够满足移动机器人对视觉处理过程的实时性要求．     

一种新颖的模糊字符图像分割自适应形态学方法

本文提出了一种新颖的字符分割以及从主要的模糊图像中提取特征矢量的自适应方法。算法基于分割分段字符之前自动检测的碎片和合并这些碎片的直方图描述。对于重叠字符的分割，形态学分级算法自动确定参考线；对于连接字符的分割，形态学细化算法和分割费用计算自动检测基准线。我们的方法可以检测碎片、重叠或连接的字符及其自适应应用。更为重要的是，在实验中，模糊图像作为容许标识的图像，以减少有效区域的使用来评估其坚固性、柔韧性以及方法的有效性。系统方式的数据输出．如特征矢量保留了较多正确的有用信息，在自动图像识别系统中作为输入数据。     

基于时序活动逻辑的复杂系统多Agent动态协作模型

动态复杂问题求解是人工智能和复杂自适应系统理论与应用重要研究领域,多Agent动态协作是研究热点和难点之一,如何将问题与任务切分有待进一步地研究.借鉴组织学思想将自适应系统中的自主运行单元抽象为Agent,把复杂自适应系统视为多Agent系统组织,从时间和状态角度对复杂动态系统的行为进行描述,提出了基于时序活动逻辑的多Agent系统动态协作任务求解自适应机制和构造模型,建立了用于协作推理的语义规则、授权规则和行为规则,通过在中国科学院智能信息处理重点实验室开发的MAGE等平台上多方实验和仿真测试,验证了方法的可行性和有效性.     

A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.     

多主体框架下结合最大期望值和遗传算法的SAR图像分割

目的 SAR图像中固有的相干斑噪声增加了图像分割的困难.为此,提出一种分布式SAR图像分割算法.方法 首先假设图像中同质区域内像素满足同一独立的Gamma分布,依此建立SAR图像模型;为了刻画SAR图像中像素的类属性,建立标号场的MRF(Markov Random Field)模型;在Bayesian理论框架下建立图像分割模型;在多主体系统(MAS)框架下,结合MRF模型和遗传算法(GA)模拟分割模型.MAS结构由分割主体和协调主体组成,其中分割主体利用最大期望值( EM)算法估计MRF模型参数,从而实现全局分割;协调主体利用GA实现全局最优.结果 为了验证提出方法的有效性,分别对模拟和RADARSAT-I/II SAR图像进行实验,并与EM和RJMCMC算法比较.本文算法的用户精度、产品精度、总精度及kappa系数均高于EM算法.定性和定量分析结果验证了本文算法的鲁棒性和有效性.结论 实验结果表明提出的分布式MAS框架下SAR图像分割方法,能够提高分割精度.该方法适用于中高分辨率单极化的SAR图像,且具有很好的抗噪性.     

基于阈值法与区域生长法综合集成的图像分割法

本文首先对一类因照明不均匀使得图像质量受到一定影响的这样一类比较复杂的图像特点及其分割难点做分析,然后提出一种新的基于阈值法与区域生长法综合集成的图像分割方法。实验结果表明,该分割算法不仅适用于较简单的图像分割问题,而且适用于分割因照明不均匀使得图像质量受到一定影响的这样一类较复杂的图像的分割,且分割时间可进一步缩短。     

Statistical region merging

This paper explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose segmentation error is, as we show, limited from both the qualitative and quantitative standpoints. This approach can be efficiently approximated in linear time/space, leading to a fast segmentation algorithm tailored to processing images described using most common numerical pixel attribute spaces. The conceptual simplicity of the approach makes it simple to modify and cope with hard noise corruption, handle occlusion, authorize the control of the segmentation scale, and process unconventional data such as spherical images. Experiments on gray-level and color images, obtained with a short readily available C-code, display the quality of the segmentations obtained.     

A multi-plane approach for text segmentation of complex document images

This study presents a new method, namely the multi-plane segmentation approach, for segmenting and extracting textual objects from various real-life complex document images. The proposed multi-plane segmentation approach first decomposes the document image into distinct object planes to extract and separate homogeneous objects including textual regions of interest, non-text objects such as graphics and pictures, and background textures. This process consists of two stages—localized histogram multilevel thresholding and multi-plane region matching and assembling. Then a text extraction procedure is applied on the resultant planes to detect and extract textual objects with different characteristics in the respective planes. The proposed approach processes document images regionally and adaptively according to their respective local features. Hence detailed characteristics of the extracted textual objects, particularly small characters with thin strokes, as well as gradational illuminations of characters, can be well-preserved. Moreover, this way also allows background objects with uneven, gradational, and sharp variations in contrast, illumination, and texture to be handled easily and well. Experimental results on real-life complex document images demonstrate that the proposed approach is effective in extracting textual objects with various illuminations, sizes, and font styles from various types of complex document images.     

Multi-feature sea–land segmentation based on pixel-wise learning for optical remote-sensing imagery

Robust sea–land segmentation in optical remote-sensing images is challenging because of the complex sea–land environment and scene diversity. Here, we propose a novel multi-feature sea–land segmentation method via pixel-wise learning for optical remote-sensing images. Multiple features such as greyscale, local statistical information, edge, texture, and structure are first extracted from each pixel in training images and then used to learn a multi-feature sea–land classifier, which transforms the segmentation issue into pixel-wise binary classification problem. In our approach, a new multi-feature sea–land segmentation algorithm is put forward based on the approximation of Newton method. Experiments on Google-Earth, Venezuelan Remote Sensing Satellite-1 (VRSS-1) and Gaofen-1 images demonstrate that the proposed approach yields more robust and accurate sea–land segmentation results.     

A syntactic method for image segmentation and object recognition

In this paper, image segmentation and object recognition using syntactic methods are investigated. The segmentation process is embedded in the parsing algorithm. The approach can be described by a syntax-directed relaxation process. Previous error-correcting parsing algorithms, however, have not tackled the segmentation problem satisfactorily. Experimental results using Synthetic Aperture Radar (SAR) images are given.     

An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation

Flower identification and recognition are tedious and difficult tasks even for humans. Image segmentation based on automatic flower extraction is an essential step for computer-aided flower image recognition and retrieval processes. Furthermore, there is a challenge for segmentation of the object(s) from natural complex background in color images. In this study, a novel performance optimization approach for image segmentation, i.e. simulated annealing-based mean-shift segmentation (SAMS), is proposed and implemented. It is based on the simulated annealing solution of quadratic assignment problem model treated as an image segmentation process using feature-based mean-shift (MS) clustering on color images. The proposed approach is designed to realize a global and unsupervised (i.e., fully automatic) segmentation. It is a modified and optimized version of Backprojection-based mean-shift segmentation (BackMS) method. In conducted segmentation experiments, the performance results of SAMS approach are compared with the ones of BackMS method. Comparison of overall performance results and statistical analysis (i.e., Wilcoxon signed rank median test) show that SAMS approach improves the performance of BackMS method. It is measured as 49.33% when using object bounding boxes and as 51.33% when using object pixel regions.     

Extraction of diffuse objects from images by means of watershed and region merging: example of solar images.

This paper presents a new method for extraction of diffuse objects from images, which was developed for segmentation of solar images obtained from extreme-UV imaging telescope (EIT) experiments of the satellite SOHO mission. As a particular type of objects to be extracted coronal holes in EIT images have been chosen. The method described is based on the use of a watershed algorithm. The result of the watershed segmentation is a partition of the whole domain of the image into a large number of small regions. These regions are then combined in a region merging process. The proposed region merging algorithm iteratively adds the darkest regions and maximizes the average contrast between a current mask and a set of its neighboring regions. We show that the maximization of the average contrast gives segmentation results that are visually acceptable. Furthermore, this approach allows us to conduct the segmentation of EIT images independently of any explicit fine-tuning parameters. The proposed method was extensively tested on EIT images obtained at various times and various levels of solar activity, and we will show that it can be used independently of the local brightness level and the extent of coronal holes.     

Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes

In this article, a new method for segmentation and restoration of images on two-dimensional surfaces is given. Active contour models for image segmentation are extended to images on surfaces. The evolving curves on the surfaces are mathematically described using a parametric approach. For image restoration, a diffusion equation with Neumann boundary conditions is solved in a postprocessing step in the individual regions. Numerical schemes are presented which allow to efficiently compute segmentations and denoised versions of images on surfaces. Also topology changes of the evolving curves are detected and performed using a fast sub-routine. Finally, several experiments are presented where the developed methods are applied on different artificial and real images defined on different surfaces.