一种基于内容的图像质量评价测度

鉴于传统的图像质量评价测度,如峰值信噪比,不能有效地反映人对图像的视觉感知。为此,提出了一种基于内容的图像质量评价测度;在改进基于结构相似度(structural similarity,SSIM)的图像质量测度基础上,根据图像的内容将图像分成边缘、纹理和平滑区域3部分,在每个区域又利用模糊积分融入了结构相似性的数量信息,从而充分利用了图像结构信息相似性及其在位置和数量上的融合信息来全面评价图像质量。实验结果表明,利用该测度所得到的图像质量评价结果与主观评价结果有着很好的相关性,能较准确地反映人对图像质量的主观感受。     

客观评价彩色图像编码质量的模糊积分方法

为了客观地评价彩色图像的编码质量．首先将彩色图像的RGB颜色模式转化成YUV颜色模式;然后在YUV颜色空间中采用图像的三构件模型．并以模糊测度和模糊积分为工具,给出了一种客观评价彩色图像编码质量的新方法．实验结果表明．该方法和人的主观评价方法MOS的相关系数为0．92．大大高于常用的峰值信噪比和MOS的相关系数0．69．     

基于Sugeno模糊积分的形态学彩色图像处理

在Sugeno模糊积分和柔性多结构基础上,提出新的彩色图像形态学滤波器和边缘检测方法。基于Sugeno模糊积分的评价值进行彩色图像点的矢量排序,通过结构元素的比较分析,得出柔性多结构元素抗噪声能力更强。与基于HSV矢量排序的方法比较,基于Sugeno模糊积分矢量排序的形态学变换效果更好。实验表明,新的形态学算法比经典形态学算法能更有效地去除图像的噪声和获取彩色图像边缘,保留图像细节。     

基于模糊相似度量的图像检索

提出一种基于Sugeno模糊积分的模糊相似度量并用于图像检索中。文章用模糊测度来描述人的主观反馈。试验表明该文的方法可以大大提高图像检索系统的效率和稳定性,在反馈后的表现要优于加权平均方法(WAO)和采用Choquet积分(CI)的方法。     

基于共识与Choquet积分的模糊Petri网可信度确定方法*

针对模糊Petri网可信度确定过程中,决策结果可接受程度较低和未考虑影响因子间关联的问题,提出基于群体共识测度与Choquet积分确定模糊Petri网可信度的方法。设计了基于决策者与群体评估信息偏差修正的自适应共识测度算法,获取可接受程度较高的群体评估信息。构建最大2-可加模糊测度Marichal熵模型求解影响因子间的交互作用系数,根据交互作用系数、默比乌斯变换和模糊测度之间的关系确定模糊测度,利用Choquet积分算子集结群体评估信息得到模糊Petri网可信度。将此方法用于确定燃气轮机故障诊断模糊Petri网输入库所可信度,并与未考虑共识测度和影响因子间关联的方法进行比较,计算结果验证了该方法的有效性和可行性。     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

基于小波系数模糊积分的图像融合算法研究

提出一种新的基于Choquet模糊积分的图像融合方法。首先利用小波系数的区域特征获取模糊积分的信度函数,其次根据局部窗口内的模糊边缘评价函数自适应地构造模糊密度,最后由Choquet模糊积分确定融合后的小波系数,从而得到融合图像。将此方法应用于微光与红外图像融合处理中,实验结果表明,Choquet模糊积分法可以使得最终的融合图像在空间细节信息的增强和光谱信息的保持两方面的综合性能得到提高。     

Fuzzy Integral Filters: Properties and Parallel Implementation

Fuzzy integrals as image filters provide a standard representational form which generalize linear filters such as the averaging filter, morphological filters such as flat dilations and erosions, and order statistic filters such as the median filter. However, fuzzy integral filters are computationally intensive. Computing the output value obtained by fuzzy integral filtering at a point involves sorting all the pixels in a neighborhood of the point according to their values and then computing ordered weighted sum or maximum with respect to an appropriate fuzzy measure. In this paper we discuss some properties of fuzzy integral filters and describe a method for enhancing the processing elements of single instruction, multiple data (SIMD) mesh computers with comparators and counters to efficiently implement fuzzy integral filters.     

IMAGE ANALYSIS AND FUZZY INTEGRATION APPLIED TO PRINT QUALITY ASSESSMENT

ABSTRACT

We present an image analysis and fuzzy integration based option for the assessment of print quality in rotogravure printing. Values of several print distortion attributes are evaluated employing image analysis procedures and then are aggregated into an overall print quality measure using fuzzy integration. The experimental investigations performed have shown that the print quality evaluations provided by the measure correlate well with the print quality rankings obtained from the expert. The developed tools are successfully used in printing shops for routine print quality control.     

Detection of heart valve diseases by using fuzzy discrete hidden Markov model

In the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and λ fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors.     

基于模糊积分和粒子群算法的多分类器融合

使用模糊积分的方法将多个分类器进行融合可以提高分类精度,但是如何得到最优的模糊测度是一个尚未解决的问题。本文根据模糊测度Sugeno积分的理想特性,用模糊测度代替各个分类器的权值,利用粒子群算法全局搜索的优势,将模糊测度对应于粒子,并随速度和位置并不断调整,从而得到全局最优的模糊测度。通过仿真实例验证了新的多分类器融合模型具有较低的分类错误率,并能有效地提高分类精度。     

基于Haar小波变换高频特征的图像质量评价算法

在分析人类视觉系统(HVS)特性的基础上,提出一种基于Haar小波变换高频特征的图像质量评价算法(CSSIM)。此方法首先将图像进行小波变换得到不同方向和不同频率的4个频带划分,在3个高频频带比较边缘结构信息上根据人类视觉系统特性,赋予3个频带比较结果相应视觉权重值,最终得到归一化的图像质量评价指标CSSIM。通过非线性回归拟合分析,证明了提出的算法(CSSIM)相对图像结构相似度评价算法(MSSIM)及其改进算法(MGSSIM)更符合人类的主观视觉感受。     

Sugeno integral generalization applied to improve adaptive image binarization

Classic adaptive binarization methodologies threshold pixels intensity with respect to adjacent pixels exploiting integral images. In turn, integral images are generally computed optimally by using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images. Which, in turn, this technique is supported by an efficient design of a modified SAT for generalized Sugeno fuzzy integrals. We define this methodology as FLAT (Fuzzy Local Adaptive Thresholding). Experimental results show that the proposed methodology produced a better image quality thresholding than well-known global and local thresholding algorithms. We proposed new generalizations of different fuzzy integrals to improve existing results and reaching an accuracy $\approx 0.94$ on a wide dataset. Moreover, due to high performances, these new generalized Sugeno fuzzy integrals created ad hoc for adaptive binarization, can be used as tools for grayscale processing and more complex real-time thresholding applications.     

Extremal fuzzy integrals

Fuzzy integral is a monotone idempotent functional on a fuzzy measure. The extremal fuzzy integrals are introduced. Regular fuzzy integrals do not distinguish functions not distinguishable by the underlying fuzzy measures. These integrals include Choquet, Sugeno, Shilkret and several other well known integrals.     

基于局部不变特征的图像质量评价

针对结构相似度算法在感知图像质量时采取平均加权策略的不足,利用人眼对图像中不同区域的关注程度不同的特性,提出了基于局部不变特征的图像质量评价算法。该算法在失真图像结构相似度质量分布图的基础上,提取图像的局部不变特征点,将这些特征点周围一定区域赋予较大的视觉权重,最后运用综合加权策略来衡量失真图像的质量。在标准图像测试库上的实验结果表明,该算法计算复杂度相对较低,较大地提高了结构相似度算法的评价效果,与人眼主观感知图像质量取得了更好的一致性。     

基于Sugeno模糊积分神经网络分类器融合方法在手写数字识别中的应用

神经网络是模式识别中一种常见的分类器.针对同一个分类问题,构建多个分类器并把多个分类器进行融合可以提高分类系统的分类正确率、改善系统的稳健性.首先介绍了Sugeno模糊积分及Sugeno模糊积分神经网络分类器融合方法的一般原理,而后将其应用于手写数字识别,通过实际的案例验证了该融合方法的有效性和可行性.     

Genetic learning of fuzzy integrals accumulating human-reported environmental stress

In this paper, we develop models based on fuzzy integrals (both of the Choquet and Sugeno type) for accumulating annoyance by noise, odor or light caused by particular sources or activities. As underlying fuzzy measures, we have opted for k-maxitive measures (in particular 1-maxitive or 2-maxitive) as the best known crisp model points in this direction. The fuzzy measures are learnt from survey data and optimized using genetic algorithms. Attention is paid to several types of inconsistencies that typically arise in data sets collected through social surveys. Also, special care is taken to make sure that the Sugeno integral and the genetic algorithm that optimizes the associated fuzzy measure operates solely on the ordinal scale of linguistic labels.     

一种基于熵理论和中介真值度量的综合评价法

综合评判问题在生活中随处可见,综合评价方法也有数十种之多。其中,基于模糊数学的中介真值度量方法是评价多指标对象的方法之一,是一种能够有效处理模糊现象的方法,也是一种主观赋权法。为了使评价结果更加客观,提出了一种基于熵理论和中介真值度量相结合的综合评价方法,将熵理论应用到一级指标权重的获取,利用基于专家打分的层次分析法计算出二级指标权重,有效减小了中介真值度量方法中完全主观计算权值所造成的误差。最后,通过应用于某地住宅项目的质量评估,并分别与中介真值度量方法和熵权法的评估结果进行比较分析,验证所提方法的可行性及有效性。     

一种基于模糊函数的自适应平滑约束图像复原算法

为了在抑制噪声的同时保护图像的细节,提出了一种基于模糊函数的自适应平滑约束图像复原算法。该算法首先用模糊函数对图像局部区域内线元素的数量和方向进行评价,然后根据评价结果使用不同的高通滤波器构造平滑约束条件,同时使平滑约束条件随着图像复原的迭代过程不断更新,以便自适应地在图像平坦区域抑制噪声,而在存在物体边界的区域则保护细节信息。对比实验表明,此方法具有更高的收敛速度,更好的客观评价指标和主观视觉效果。     

基于结构失真分析的立体图像质量客观评价模型

在立体图像?视频系统编码、传输、解码等环节中,立体图像质量评价是图像失真的一个重要评判准则.考虑到图像结构特性能较好地反映立体图像质量变化,提出一种基于结构失真的立体图像质量客观评价模型,该模型包括左右视点图像质量和深度感知质量评价两部分.首先根据人眼对图像中不同区域的敏感度存在差异且自然图像具有一定方向奇异性的特性,得到左右视点图像质量评价值;然后利用左右视点的绝对差值图像,采用奇异值向量差异和均值偏差率来描述立体图像深度感的畸变;最后融合前面两部分的评价值得到最终的立体图像质量评价值.实验结果表明,文中模型与主观感知存在较好的一致性,对于高斯模糊、高斯白噪声、JPEG压缩、JP2000压缩以及H.264编码,其评价结果与主观感知之间的相关系数高于0.93,Spearman秩相关系数高于0.92,均方误差低于6.6.