图像的多层次模糊增强与边缘提取

在单层次模糊增强算法中,某个灰度层次附近的边缘得到了增强,同时其它的一些边缘受到了抑制。为了同时增强图像中不同灰度层次的边缘信息,提出一种多层次模糊增强算法,并且给出了快速实现算法。此外,把多层次模糊增强算法应用于图像边缘提取中,取得了优于单层次模糊增强的效果。     

基于三角隶属函数和模糊熵的图像增强方法及其相关应用研究

鉴于图像增强技术在生活应用中的重要性,模糊技术在图像应用中的实用性和广泛性,提出了一种基于三角隶属函数和模糊熵的新的图像增强算法(T-FE增强算法),使用三角函数作为隶属函数,重构参数型对比增强算子,运用模糊熵最大原则选取阈值,计算快速,简单.并且将T-FE算法运用于图像分割,边缘检测.通过实验仿真表明,T-FE算法在进行图像处理时有较好效果.     

基于熵的多目标多维模糊决策模型

顾及多目标多维决策中存在的模糊性和随机性,基于Jaynes的信息熵最大原理,提出一种模糊环境下带有主观监督因子和信息熵的目标函数,导出了新的计算模糊决策识别矩阵与目标权重的模糊交叉计算公式.该模型将基于目标的客观决策与主观决策有机结合起来,为求解最优模糊决策识别矩阵和确定目标最优权重提供了一种有效途径,并把信息熵作为评价决策优劣的指标,进一步发展了多目标多维模糊决策理论模型.将本文提出的模糊决策方法应用于16家电炉炼钢企业的模糊综合评价,取得了较为满意的效果.     

Contrast enhancement for image by WNN and GA combining PSNR with information entropy

A new contrast enhancement algorithm for image is proposed combining genetic algorithm (GA) with wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of an image, a classification criterion is proposed. Contrast type for original image is determined by the new criterion. Parameters space is, respectively, determined according to different contrast types, which greatly shrink parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of traditional histogram equalization that it reduces the information and enlarges noise and background blur in the processed image, a synthetic objective function is used as fitness function of GA combining peak signal-noise-ratio (PSNR) and information entropy. In order to calculate IBT in the whole image, WNN is used to approximate the IBT. In order to enhance the local contrast for image, discrete stationary wavelet transform (DSWT) is used to enhance detail in an image. Having implemented DSWT to an image, detail is enhanced by a non-linear operator in three high frequency sub-bands. The coefficients in the low frequency sub-bands are set as zero. Final enhanced image is obtained by adding the global enhanced image with the local enhanced image. Experimental results show that the new algorithm is able to well enhance the global and local contrast for image while keeping the noise and background blur from being greatly enlarged.     

The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties

Many multiple attribute decision analysis (MADA) problems are characterised by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) approach has been developed in the 1990s and in the recent years to support the solution of MADA problems with ignorance, a kind of probabilistic uncertainty. In this paper, the ER approach is further developed to deal with MADA problems with both probabilistic and fuzzy uncertainties.In this newly developed ER approach, precise data, ignorance and fuzziness are all modelled under the unified framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure. A new fuzzy ER algorithm is developed to aggregate multiple attributes using the information contained in the fuzzy belief matrix, resulting in an aggregated fuzzy distributed assessment for each alternative. Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation. A numerical example is provided to illustrate the detailed implementation process of the new ER approach and its validity and wide applicability.     

A novel enhancement method for low illumination images based on microarray camera

It is difficult but important to get clear information from the low illumination im-ages. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the diff erent areas of an image with contrast brightness diff erences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we gen-eralize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well. Experimental results show that the proposed method can get better image enhancement eff ect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well. The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.     

模糊度定义的不一致性及其理论分析

指出模糊度定义的不一致性，并通过实例进行验证；从理论上分析模糊度定义的不一致性产生的原因，指出当模糊度理论在图像处理中应用时模糊度定义的不一致性可能会产生的影响；给出一个比较合理的模糊度定义。文中提出的问题是在工程应用过程中发现的新问题，所做的工作也只是初步的。     

论模糊性及其数学问题

近年引发的关于模糊逻辑的争论，从根本上说触及到产生模糊性的背景和相应的数学方法问题。本文从认知科学的角度考察模糊性，认为模糊性源于人类在认识客观事物的各种认知思维活动，并在不同层次的认识思维反映过程中，形成了狭义模糊性，一般模糊性，广义模糊性和泛模糊性。文中初步了不同层次的模糊怀问题和相应的数学问题，抛砖引玉，以引起广大研究者的关注，研究和讨论。     

A health status index model using a fuzzy approach

In order to derive an operational health status index, it is necessary to develop the concept of health status. An individual belongs to one of several alternative health statuses, but it is difficult to clearly identify which, because the boundaries of statuses are not sharply defined. In this paper a fuzzy approach is proposed for use in expressing the health status and its index. We deal with health status as an intrinsically ambiguous and multi-dimensional expression which is determined through some subjective judgement. We introduce the concept of fuzziness and fuzzy set in order to study the ambiguous health status. Some concepts and properties of group fuzzy measures are clarified so that we may obtain the common measure to express the health statuses. Two examples using the researched data are presented for explaining the concept of health status, the group fuzzy measures and the health status index model.     

A fuzzy closeness approach to fuzzy multi-attribute decision making

The aim of this paper is to develop a new fuzzy closeness (FC) methodology for multi-attribute decision making (MADM) in fuzzy environments, which is an important research field in decision science and operations research. The TOPSIS method based on an aggregating function representing “closeness to the ideal solution” is one of the well-known MADM methods. However, while the highest ranked alternative by the TOPSIS method is the best in terms of its ranking index, this does not mean that it is always the closest to the ideal solution. Furthermore, the TOPSIS method presumes crisp data while fuzziness is inherent in decision data and decision making processes, so that fuzzy ratings using linguistic variables are better suited for assessing decision alternatives. In this paper, a new FC method for MADM under fuzzy environments is developed by introducing a multi-attribute ranking index based on the particular measure of closeness to the ideal solution, which is developed from the fuzzy weighted Minkowski distance used as an aggregating function in a compromise programming method. The FC method of compromise ranking determines a compromise solution, providing a maximum “group utility” for the “majority” and a minimum individual regret for the “opponent”. A real example of a personnel selection problem is examined to demonstrate the implementation process of the method proposed in this paper.     

A modified fuzzy clustering for documents retrieval: application to document categorization

The paper advocates the use of a new fuzzy-based clustering algorithm for document categorization. Each document/datum will be represented as a fuzzy set. In this respect, the fuzzy clustering algorithm, will be constrained additionally in order to cluster fuzzy sets. Then, one needs to find a metric measure in order to detect the overlapping between documents and the cluster prototype (category). In this respect, we use one of the interclass probabilistic reparability measures known as Bhattacharyya distance, which will be incorporated in the general scheme of the fuzzy c-means algorithm for measuring the overlapping between fuzzy sets. This enables the introduction of fuzziness in the document clustering in the sense that it allows a single document to belong to more than one category. This is in line with semantic multiple interpretations conveyed by single words, which support multiple membership to several classes. Performances of the algorithms will be illustrated using a case study from the construction sector.     

Interactive fuzzy linear programming and an application sample at a textile firm

The purpose of this study is to examine Interactive Fuzzy Linear Programming (IFLP) model by using Zimmermann, Werners, Chanas and Verdegay’s approaches that provide best decision-making under fuzzy environments. In this study, it is used the method which can model the fuzzy structure of the real world and which operates with the decision maker interactively, which aims at obtaining the best solution by continuing this interactiveness in the solution process, which includes fuzziness with more realistic approach to the system. It is showed that the importance of fuzziness concept for IFLP problems, how it is applied on real-world problems and its effects.     

模糊支付矩阵混合策略模型及其求解

在各局中人没有最优纯策略时,考虑选取不同策略的概率分布和支付矩阵的模糊性,建立模糊支付混合策略的对策模型.基于线性规划和三角模糊数排序准则,给出模型求解方法.模型在实例中得到验证,其计算简单,结果合理,可以为决策提供科学依据.     

基于模糊随机变量的结构可靠性分析

研究了广义应力和广义强度同时具有模糊性和随机性时的结构可靠度计算问题,基于模糊随机变量和模糊随机事件的理论,建立了结构模糊可靠度的计算模型.最后通过一算例,验证了该方法的有效性和合理性.     

Methods of critical value reduction for type-2 fuzzy variables and their applications

A type-2 fuzzy variable is a map from a fuzzy possibility space to the real number space; it is an appropriate tool for describing type-2 fuzziness. This paper first presents three kinds of critical values (CVs) for a regular fuzzy variable (RFV), and proposes three novel methods of reduction for a type-2 fuzzy variable. Secondly, this paper applies the reduction methods to data envelopment analysis (DEA) models with type-2 fuzzy inputs and outputs, and develops a new class of generalized credibility DEA models. According to the properties of generalized credibility, when the inputs and outputs are mutually independent type-2 triangular fuzzy variables, we can turn the proposed fuzzy DEA model into its equivalent parametric programming problem, in which the parameters can be used to characterize the degree of uncertainty about type-2 fuzziness. For any given parameters, the parametric programming model becomes a linear programming one that can be solved using standard optimization solvers. Finally, one numerical example is provided to illustrate the modeling idea and the efficiency of the proposed DEA model.     

模糊美学的一种认知结构

美学问题存在大量的模糊性现象。本文分析了建立在处理狭义模糊性的L.A.Zadeh模糊集合论基础上的传统的模糊美学。运用模糊性层次的分析方法探讨了模糊美学问题,提出了模糊美学的一种认知结构,提出了结构性模糊美学的概念,讨论了它的本质、特征、研究对象和范围、研究任务和方法。     

Sufficiency and fuzziness in random experiments

In previous papers, the consequences of the “presence of fuzziness” in the experimental information on which statistical inferences are based were discussed. Thus, the intuitive assertion «fuzziness entails a loss of information» was formalized, by comparing the information in the “exact case” with that in the “fuzzy case”. This comparison was carried out through different criteria to compare experiments (in particular, that based on the “pattern” one, Blackwell's sufficiency criterion). Our purpose now is slightly different, in the sense that we try to compare two “fuzzy cases”. More precisely, the question we are interested in is the following: how will different “degrees of fuzziness” in the experimental information affect the sufficiency? In this paper, a study of this question is carried out by constructing an alternative criterion (equivalent to sufficiency under comparability conditions), but whose interpretation is more intuitive in the fuzzy case. The study is first developed for Bernoulli experiments, and the coherence with the axiomatic requirements for measures of fuzziness is also analyzed in such a situation. Then it is generalized to other random experiments and a simple example is examined.     

基于混合单纯形算法的模糊均值图像分割

针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度.