Image edge detection algorithm in fuzzy domain

An image edge detection algorithm in fuzzy domain is proposed, which combines adaptive fuzzy enhancement and multi direction fuzzy morphology to detect the edges of fuzzy image. The adaptive fuzzy enhancement method enhances the fuzzy image within blocks with sliding windows to avoid losses of the real edges resulting from enhancing with single threshold for the whole image and lead strong adaptive ability to image region variance. The multi direction fuzzy mathematical morphology operates on the enhanced fuzzy image with structure elements of multiple different directions to extract the real edges with directionality and restrain non directional noise. Experiments show the algorithm can detect fuzzy image edge effectively with strong antinoise ability.     

Multi scale and multi orientation features for image de noising

An adaptive algorithm for image de noising is proposed based on the multi scale and multi orientation features. The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform. Then thresholds functions are adaptively set with these coefficients. The texture of the image information is introduced by using the mean of decomposition scale and the energy of regional. The greater the energy, the more information of the texture while the same decomposition scales, the smaller the threshold is set. On the contrary, the greater the threshold is set. After the de noising and then reconstruction of these coefficients, image de noising is implemented. Compare to the wavelet transform threshold and contourlet transform threshold, the nonsubsampled contourlet transform pick up the image detail better and improve the quality of the image.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

An edge detection method based on a good point set genetic algorithm

In order to improve the convergence rate of genetic algorithms based on edge detection, a novel edge detection method based on a good point set genetic algorithm (GGA) was proposed. The proposed method designed the crossover operation with the theory of good point set in which the progeny inherits the common genes of the parents which represent its family so as to improve the convergence rate of the genetic algorithm. Furthermore, before the algorithm was used for edge detection, the feature space of the image grey level was transformed into the feature space of the fuzzy entropy. Dissimilarity enhancement processing next was applied to the image by using a fuzzy entropy theory to filter the non edge pixels so as to reduce the scale of the solution domain. This approach offered another efficient way to improve the convergence rate. Experimental results show the proposed algorithm performs very well in terms of convergence rate. The detected edge image is well localized, thin, and robustly resistant to noise.     

Segmentation of Color Image Based on Feature Divergence and Fuzzy Theory

In order to improve general adaptive capability of algorithm,the new color image segmentation algorithm based on feature divergence and fuzzy theory(FDCIS) is proposed.The algorithm introduces feature divergence and fuzzy dissimilarity function into calculation in order to measure the dissimilarity of feature vector,clusters data by means of feature divergence,and accomplishes the merge of image region.The experimental results demonstrate that the color image segmentation result of the proposed approach reduce calculation on large sample of color image,simply and effectively solve over-segmentation of color image,avoid the dependence of the algorithm on initial condition,and hold favorable consistency in terms of human perception.     

Multicriterion Optimization for Projection Data in Computerized Tomography

It is an effective way for improving quality of reconstructed images that projection data are optimized in the field of Computerized Tomography (CT). For lowering the fuzziness on projection data caused by lots of factors such as noise and getting the unigue optimal reconstructed image from noisy projection data further more, in this paper, the authors develop a new model named Multicriterion Optimization Model (MOP) for optimizing projection data. The model is structured based on the theories of fuzzy mathematics and decision making, via the method of deducing projection fuzzy exponent function and square error fuzzy exponent function. The experiments for validating the model have been carried out on personal computer (PC). At first, we make simulation collecting in the images presented and add Gauss Noise into the projection data obtained by simulation collecting, Then, complete the image reconstruction from noisy projection data by the solution without optimization and another solution with multicriterion optimization. Finally, compare and analysis the different results about images reconstructed by two different solutions. The experiment results indicate that the MOP in this paper has better consistency with the theory and practice as well as obvious advantage of antinoise ability.     

Corner Detection Algorithm Based on Fuzzy Subjection Degree

Corner detection in planar curves is a basic problem in image processing domain. The authors start from concluding corner's intuitive characters, introduce fuzzy set theory, and propose a multi-feature based algorithm which contains fuzzy subjection degree to achieve the goal. First, two simple fuzzy characters are addressed which compile with human perception. Second, three formulations corresponding to corner features are presented, and they are synthesized and summarized into a principle used to detect and locate corners. At last, by comparing computed results with existed results, robustness and effectiveness of our algorithm are proved.     

Multi-dot Synthetic Full-parallax Hologram

The synthetic holography is combined with the digital processing technology of the computer; full-parallax intensity information of a three-dimensional object has been obtained and encoded accurately and effectively by the computer. The encoded information from different visible units of the object is recorded with single-wavelength laser successively on the different dot area of one holographic film. When the obtained film is illuminated by a common lamp, the different intensity information of two view-angles is displayed in each eye, so the stereoscopic image can be obtained. When the line of sight change horizontally or vertically in observation area, the full-parallax reconstructed image will be observed.     

Corner detection based on evolution difference of plannar curves in B spline scale space

In order to improve image corner detection precision and efficiency, a novel corner detection algorithm based on B spline scale space evolution difference is proposed. The norm of DoB is defined as corner response function to evaluate the multi scale evolution difference. The DoB corner detector confluents the image boundary features with different scales, which can not only strengthen the response of the feature points, but also depress the influence of noise. Among all corner detection algorithms based on B Spline, the proposed algorithm is relatively lower for computation complexity and faster. The comparative experiments demonstrate that the proposed algorithm works well for localization, robustness against noise, as well as invariance to rotation and scalability.     

Illumination compensation of color video for driver fatigue monitor

A robust method to compensation illumination of driver video color image to enhance image quality has been proposed. Firstly, the original image is decomposed into high-frequency image and low-frequency image based on spatial image decomposition. The illumination information is completely contained in the low-frequency image. Next, low-frequency image is compensated by using Retinex approach which can brighten the darker area. Then, the compensated low-frequency image and high-frequency image are synthesized. Finally, new image is obtained by illumination balance of synthesized image. Results show that the algorithm has better results compared with the traditional algorithm.     

Novel Image-adaptive Watermarking Algorithm

Digital watermarking is an effective method to provide copyright protection for digital media. A blind image- adaptive watermarking algorithm based on DCT domain is presented. The algorithm selects adaptively the step size of quantization to embed the watermark bits by modifying DC components via the human visual system model and local characteristics of image; at the same time some AC components of the blocks with less perceptual capacity are also modified to embed the same watermark bits by using quantization. Thus the relation between robustness and imperceptibility can be well balanced. The algorithm recovers the watermark without any reference to the original image. Compared with the similar algorithm reported in the literature, this method can give better image quality and is better robust against noise and commonly used image processing techniques.     

快速台风“海鸥”强度、路径特点及成因分析

为了进一步认识西北行台风活动特点成因,为此类台风预报提供参考,利用1°×1° NCEP再分析资料、卫星云图资料及实时定位定强数据,对台风“海鸥”的路径、强度特征及成因进行分析。结果表明：“海鸥”活动主要受副高外围气流操纵,不同阶段受不同层次引导气流影响。引导气流强度越大台风移速越快,其波动可能是台风路径摆动的重要原因。高海温是“海鸥”生成及发展的基础条件;“海鸥”移动下游存在弱垂直风切变区域、西南季风与越赤道气流卷入、水平风场不对称分布是“海鸥”强度加强的重要原因;同时指出“海鸥”不同阶段,感热和潜热所起作用不同。此外进一步探讨了“海鸥”成熟期仅维持台风强度的可能原因。     

An ALgorithm of the Second Kind Fuzzy Reliability

Based on L.A.Zadeh's idea of fuzzy probability operation in his language probability which space is fuzzy probability,the concrete algorithm of the second kind fuzzy reliability of engineering structure/system is researched.The calculating equation of the fuzzy probabiliy of the second kind fuzzy reliability is given out by using the extension principle for fuzzy sets.The fuzzy set decompose theorem,which satesfies the closed probability operation,is given out and proved.The method calculating fuzzy reliability of that structure/system is studied,where the resistance and load effect of the structure/system have probability.The analysis indicates that the calculation of the second kind fuzzy reliability for a structure/system can be transformed into a series of solving optimum programming.The result of the example indicates the rationality of above algorithm.     

Three dimensional temperature field visualization reconstruction of charge coupled device radiation image

Aiming at the questions of raceway section two dimension temperature distribution from two dimensional image accumulated by three dimensional radiation space and three dimensional temperature reconstruction error caused by limited charge coupled device (CCD) sensors for a sudden spread combustion chamber, the two dimension rectangle interpolation virtual CCD sensor installation algorithm is provided as a new technology for solving the difficulties of installing more CCD sensors and filtered back projection reconstruction error caused by limited projection numbers. The radiation intensity projection data got from flame image collected from the installed CCD sensors and computed virtual CCD sensors is interpolated for filtered back projection to computer intensity distribution and calibrated bi color method is used to calculation temperature in raceway depth direction. The results of experiments on a mini type coal gas furnace and practice test showed that the algorithm is accurate and credible and realized the on line monitoring for sudden spread combustion chamber raceway depth direction. At the same time, the useful information is provided for further studying combustion working state for the sudden spread combustion chamber.     

应用数字图像技术对不同株型夏玉米进行高温胁迫诊断

以受高温胁迫程度不同的夏玉米为研究对象,利用数码相机获取玉米吐丝-授粉期冠层的数字图像,同时利用叶绿素荧光仪探测植株受胁迫的程度,研究利用数码相机的可见光光谱进行夏玉米受逆境胁迫程度诊断的可行性。结果表明,红光标准化值与叶片叶绿素荧光值(Fv/Fm)成负相关关系(r=-0.35),而绿光标准化值、蓝光标准化值均与Fv/Fm成正相关关系,其相关系数r分别为0.57、0.70。由此可以得出,绿光标准化值、蓝光标准化值能反映夏玉米受胁迫程度,其中蓝光标准化值是能够较好地反映夏玉米受高温胁迫程度的光谱参数。     

A New Method of Diesel Engine Fuel System Fault Diagnosis

A method of diesel engine fuel system fault diagnosis based on wavelet transform and fuzzy C-means clustering is presented. Five characteristic parameters of reflecting fault state are distilled with wavelet transform of pressure wave of high-pressure oil pipe of diesel. The theory and generic approach of fuzzy C-means clustering algorithm (FCM) is given, and the validity of evaluating fuzzy clustering making use of partition coefficient, partition entropy and parting coefficient is pointed out. Identification of fault mode can be completed utilizing standard fault character modes established by FCM algorithm, and calculating and comparing the similarity degree between this standard mode and sample. The arithmetic is applied to all kinds of typical faults diagnose in the diesel engine fuel system. Measuring results indicate that the precision of fault diagnosis is increased with the analysis of wavelet and FCM.     

Drift error compensation algorithm for fiber optic gyro based on T-S fuzzy modeling

A drift error nonlinear compensation algorithm for Fiber Optic Gyro (FOG) is presented based on T-S fuzzy model with the antecedent parameters identified by G-K clustering algorithm and the error model of T-S fuzzy model with the consequent parameters identified by least square algorithm. The computed results show that this model can compensate the original data effectively, while the error principles of FOG do not need to be understood well. Comparing with the original data, compensation with linear fitting and compensation with neural network, the absolute error of the proposed model reduces by 99%, 96% and 10%, respectively. The error variance reduces by 99%, 98% and 20%, respectively. The results indicate that this proposed algorithm can be simply operated with high precision and easy to realize in engineering.     

Generalized Damage Reliability and Sensitivity Analysis on Rolled-Concrete Gravity Dam

Based on fuzzy self-adapting stochastic damage model, the characteristics of generalized damage stress field on material crack tip are studied. The results express that the numerical algorithm is reliable. Sensitivity of rolled-concrete gravity dam under fuzzy-stochastic damage gaining is implemented by considering the spatial variation of material parameters. It is found that the compression strength of rolled-concrete is the governing index on gravity dam reliability. The activation simulations on fuzzy self-adapting stochastic damage model were carried by adopting different fuzzy distributions, which indicated the governing characteristics of the distribution on generalized damage evolution of rock-like material.     

模糊控制系统的垂直切换判决算法分析

针对模糊控制系统中切换时延较长和切换次数较多的问题,研究了一种基于模糊控制系统的垂直切换判决算法。在基于信号强度判决时将RSS作为门限值,提高系统判决能力;同时,将网络参数和服务类型作为判决因素,结合层次分析法引入并行的模糊控制系统,缩短了切换判决的时间、选择适合用户的最佳网络,做出垂直切换。仿真结果表明,该算法减少了切换次数,降低了切换时延,增强了系统的性能。与传统的切换算法相比较,该算法对切换的判决因素考虑的更全面,兼顾了用户终端的使用环境及成本问题,有效地保证了网络的服务质量。     

The Finite Element Method for Frame Structure with Fuzzy Elastic Connections

The connections of frame structure elements at a node are ofen an elastic connection between hinged and rigid.In this paper,the stiffness matrix and the loading transfer matrix of frame element with turn elastic connections is deduced,the cross shear effect has been taken into consideration simultaneously.The stiffness matrices of some bar elements,such as both ends of the bar are hinged or rigid and one end is hinged but the other is rigid,are special cases of the stiffness matrices presented in this paper.After pointing out that the elastic coefficient at a node of a bar element with elastic joint is really a fuzzy quantity,the fuzzy stiffness matrix of the bar element is obtained,and a solution to structural fuzzy finite element equilibrium equations is presented.The fuzzy solution obtained from above is not only containing the solution of ordinary finite element method but also providing some additional informations with practical value.[WT5HZ]