GENETIC ALGORITHM WITH FUZZY FITNESS EVALUATION

Using a fuzzy estimator to evaluate the fitness of chromosomes in a genetic algorithm and adaptively training it in the evolutionary process, the genetic algorithm with fuzzy fitness evaluation is proposed to reduce the computation time of the algorithm. An analysis on the optimization performance of the proposed algorithm shows that it maintains good performance with its computation time saved. Finally, simulation results on design of a fuzzy controller are presented.     

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar （POLSAR） data. This technique is a combination of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means （FCM） clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classification accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by experiments using SIR-C/X-SAR （Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar） data.     

Edge-detection based on the local variance in angiographic images

This paper presents a new method for detection of edges in digital angiographic images. It is found that variances of local regions across edges of images are statistically different from that of those where no edge is crossed. This difference can be utilized for the detection of edges of angiographic images. An algorithm based on local variance is proposed. As a result, the edge-detection algorithm is not sensitive to noise and low-level textures of images. A computer program based on the new algorithm has been developed and used by several hospitals.     

Super-resolution reconstruction of images based on uncontrollable microscanning and genetic algorithm

Aiming at these disadvantages like lack of details, poor contrast and blurry edges of infrared images reconstructed by traditional controllable microscanning super-resolution reconstruction （SRR）, this paper proposes a novel algorithm, which samples multiple low-resolution images （LRIs） by uncontrollable microscanning, and then uses LRIs as chro- mosomes of genetic algorithm （GA）. After several generations of evolution, optimal LRIs are got to reconstruct the high-resolution image （HRI）. The experimental results show that the average gradient of the image reconstructed by the proposed algorithm is increased to 1.5 times of that of the traditional SRR algorithm, and the amounts of information, the contrast and the visual effect of the reconstructed image are improved.     

FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.     

A fuzzy-logic control algorithm for active Queue Management in IP networks

Active Queue Management （AQM） is an active research area in the Internet community. Random Early Detection （RED） is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator （NS） simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router＇s ability in network congestion control in IP network.     

Rayleigh-distribution based minimum error thresholding for SAR images

This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.     

DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization

Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival(DOA). In this method, a genetic algorithm(GA) and fuzzy discrete particle swarm optimization(FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical(AIC), particle swarm optimization(PSO), and genetic algorithm with particle swarm optimization(GA-PSO).     

ON MARKOV RANDOM FIELD MODELS FOR SEGMENTATION OF NOISY IMAGES

Markov random field(MRF) models for segmentation of noisy images are discussed. According to the maximum a posteriori criterion, a configuration of an image field is regarded as an optimal estimate of the original scene when its energy is minimized. However, the minimum energy configuration does not correspond to the scene on edges of a given image, which results in errors of segmentation. Improvements of the model are made and a relaxation algorithm based on the improved model is presented using the edge information obtained by a coarse-to-fine procedure. Some examples are presented to illustrate the applicability of the algorithm to segmentation of noisy images.     

A Virtual Walk along Street Based on View Interpolation

Image morphing is a powerful tool for visual effect. In this paper, a view interpolation algorithm is proposed to simulate a virtual walk along a street from start position to end position. To simulate a virtual walking view needs to create new appearing scene in the vision-vanishing point and disappearing scene beyond the scope of view. To attain these two aims we use two enhanced position parameters to match pixels of source images and target images. One enhanced position parameter is the angular coordinates of pixels. Another enhanced position parameter is the distances from pixels to the vision-vanishing point. According to the parameter values, pixels beyond the scope of view can be ＂moved＂ out in linear interpolation. Result demonstrates the validity of the algorithm. Another advantage of this algorithm is that the enhanced position parameters are based on real locations and walking distances, so it is also an approach to online virtual tour by satellite maps of virtual globe applications such as Google Earth.     

基于直方图的图像模糊聚类分割算法

针对传统模糊C-均值聚类算法对复杂的医学、遥感图像难以获得满意分割效果问题,将图像模糊C-均值聚类引入图像分割问题研究中,提出了基于直方图的图像模糊聚类快速分割算法。将越南学者Le提出的分布式图像模糊聚类算法目标函数进行简化,得到图像模糊聚类算法目标函数;采用拉格朗日乘子法获取其迭代求解所对应的隶属度、中立度、拒分度和聚类中心表达式,设计图像模糊聚类算法并对其收敛性进行了证明。通过复杂医学和遥感图像的分割测试结果表明,新的分割算法相比现有的模糊C-均值聚类分割算法和直觉模糊C-均值聚类分割算法具有更好的分割性能。     

结合脉冲耦合神经网络与模糊算法进行四值图像去噪

该文研究了如何将模糊算法用于脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN),进行四值图像去噪,提出了基于模糊PCNN的图像去噪算法.计算机仿真结果表明,将模糊算法与PCNN相结合,可有效地去除被噪声污染的四值图像的噪声,且恢复图像的视觉效果明显地好于用另两种常用的图像去噪方法(中值滤波和均值滤波)得到的结果.在医用图像和军事图像处理方面,四值图像的去噪恢复是非常有价值的,故本文对于PCNN的理论研究和实际应用均有重要的意义。     

基于模糊理论和CLAHE的雾天图像自适应清晰化算法

为了解决雾天图像低对比度的问题,提出了一种基于模糊理论和CLAHE的雾天图像的自适应清晰化算法.此算法结合图像的均值和标准差,将雾天图像从空域转换到模糊域,采用模糊增强算法实现全局雾天图像的自适应对比度增强后再采用有约束的局部直方图算法对雾天图像的亮度分量进行处理,在空域内进一步实现雾天图像的对比度增强.实验结果表明,该算法将模糊域和空间域的方法相结合,可以提高雾天图像的亮度和对比度,使雾天图像的视觉效果得到一定改善.     

基于改进Lazy Snapping算法的红外图像分割方法研究

针对红外图像含大量噪声以及对比度低等特点,提出一种结合快速模糊C均值聚类的改进Lazy Snapping分割方法。对红外图像使用快速模糊C均值聚类算法进行预分割,通过形态学骨架提取的方法在图像中标记出目标和背景种子点,将Lazy Snapping算法由全局分割转化为聚类区域分割,并构造能量函数,通过最小割算法求解能量函数的最小值并使分割效率得以提升,减少了图像存在的过分割现象,使Lazy Snapping算法由交互式算法变为非交互式算法,实现了红外图像的自动分割,提高了Lazy Snapping算法的实时性。通过对各类不同红外图像进行分割实验,再与其他分割方法进行性能评价比较,结果表明改进的算法具有良好的分割效果及较强的鲁棒性。     

Massive-scale visual information retrieval towards city residential environment surveillance

Urban residential environment surveillance plays an important role in modern intelligent city. Satellite images have been applied in various fields, and the analysis and processing of satellite images has become an important means to obtain the information perceived by satellites. This paper focuses on city residential environment surveillance based on massive-scale visual information retrieval. Since the shortcomings of low contrast, blurred boundary, large amount of information and susceptibility to noise, the performance of satellite image segmentation is not satisfactory, which will affect residential environment surveillance. We design an improved rough set fuzzy C-means clustering algorithm combined with ant colony algorithm. More specifically, satellite images are classified based on the gradient of pixels according to the indistinguishable relation of the image combined with rough set theory. Then, the traditional fuzzy set-based fuzzy C-means clustering algorithm is applied to the satellite image segmentation technology. Subsequently, the improved algorithm-quantum ant colony algorithm and rough set fuzzy clustering C-means algorithm are combined to achieve accurate segmentation of satellite images. Afterwards, we propose a satellite image retrieval algorithm, which can assist city residential environment surveillance. Comprehensive experiment show that our proposed method is effective and robust in residential environment surveillance.     

基于模糊核优化的图像盲去模糊算法

针对图像的模糊算法优化问题,首先选取高斯分布拟合自然图像的分布特性,利用双边滤波器从模糊图像中提取出清晰的图像边缘。针对降噪进行参数设置,在初步估计出模糊核之后,对模糊核进行正规化修正工作。最后在图像复原阶段,利用优化的凸函数拟合自然图像分布,并利用快速傅里叶变换提高算法计算速度。实验结果,表明该模糊核优化算法与现有的其他算法相比,复原后的图像具有更好的视觉效果,且计算时间减少约20%。     

印制电路板光电图像边缘信息提取

针对印制电路板(printed circuit board, PCB)光电图像模糊且含噪声的具体情况,提出了改进的边缘信息提取算法。首先分别对自适应模糊集增强算法与数学形态学边缘检测算法(edge detection algorithm of mathematical morphology, EDAMM)实施改进,并分析了其基本原理。然后结合这两种算法对PCB光电图像进行预处理及边缘信息提取。最后对两幅由不同成像系统获取的PCB光电图像进行了边缘信息提取实验。结果表明：用本文算法获得的PCB光电图像明暗对比度较高,并提取了精确且清晰的图像边缘信息,明显减少了噪声,所得图像的优质系数较高,两幅图像的优质系数分别是0.885 2、0.874 9,均高于本文中所提到的另外4种算法的结果。可见,采用本文算法可以更好地去除PCB光电图像中的模糊与噪声,并精确地提取出PCB光电图像的边缘信息。     

Classification of anatomical structures in MR brain images using fuzzy parameters

We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses low-level image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use single-echo MR images because of their high volumetric resolution; but even though we are working with only one image per brain slice, we have multiple sources of information on each pixel: absolute and relative positions in the image, gray level value, statistics of the pixel and its three-dimensional neighborhood and relation to its counterpart pixels in adjacent images. We have validated our algorithm for ease of use and precision both with clinical experts and with measurable error indexes over a Brainweb simulated MR set.     

Realizations of fast 2-D/3-D image filtering and enhancement

This paper proposes a novel algorithm for multidimensional image enhancement based on a fuzzy domain enhancement method, and an implementation of a recursive and separable low-pass filter. Considering a smoothed image as a fuzzy data set, each pixel in an image is processed independently, using fuzzy domain transformation and enhancement of both the dynamic range and the local gray level variations. The algorithm has the advantages of being fast and adaptive, so it can be used in real-time image processing applications and for multidimensional data with low computational cost. It also has the ability to reduce noise and unwanted background that may affect the visualization quality of two-dimensional (2-D)/three-dimensional (3-D) data. Examples for the applications of the algorithm are given for mammograms, ultrasound 3-D images, and photographic images.     

Adaptive fuzzy segmentation of magnetic resonance images

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.