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.     

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.     

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.     

An improved morphological edge detection algorithm of medical image

Medical images usually contain much noise which affects the edge detection accuracy. Focusing on this problem, based on the edge detection operator in mathematical morphology, an improved edge detection algorithm is presented by combining the features of the multi structure elements and the multi scale edge detection algorithm. The algorithm performs opening and closing operations on the data with the alternative sequence filters and the structure elements. The weighting operation is applied with different weight coefficients for horizontal, vertical and diagonal directions, while the edge detection operator with dilation type is calculated to obtain the improved edge detection algorithm. The steps of the algorithm are described. The algorithm is used to extract the edge of MRI image as well as the image of Lena. The experimental results indicate the algorithm can considerably improve the edge resolution of the traditional morphological edge detection methods and is practical.     

Wavelet Edge Detection Method Based-on Multi-scale Fuzzy Logic

A novel edge detection algorithm is proposed by implementing fuzzy image enhancement at different scales.The implement of this algorithm can reduce the negative influence bring from the non-maximum suppression algorithm which select only one threshold.The success of the approach depends on the selection of a general fuzzy operator(GFO) for the enhancement of contrast of region after smoothing of image at different scales.Consequently,it is more obvious that coarse intensity changes are obtained at a large scale whereas the fine details of intensity changes are obtained at a small scale.A pretty performance is obtained by used this work.     

Improvement and Application of Immune Genetic Algorithm in Structural Damage Identification

In order to solve structural multi-damage identification problem, a two-stage method based on Bayesian theory and immune genetic algorithm (IGA) is presented. Firstly, structural modal strain energy and frequency are considered as two kinds of information sources, and Bayesian theory is utilized to integrate the two information sources and preliminarily detect structural damage locations. Then, immune genetic algorithm is used to identify structural damage extents. Considering the convergence rate of basic IGA is still not very good, some improved strategies, such as culture vaccine, two termination conditions, are presented. It is shown that the two-stage method can precisely identify structural damage locations and extent, and the calculated results of the proposed improved IGA are obviously better than those of both the basic IGA and simple genetic algorithm.     

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.     

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.     

基于信息熵理论的模糊综合评价方法及其在棉花区试中的应用

为提高对区域试验参试品种评价的科学性和合理性,采用模糊数学和信息熵理论,提出了作物品种模糊熵权综合评价方法。该法利用作物品种区试的综合性状指标,建立相应的评价指标矩阵,然后借助信息论中的熵权理论,利用各评价指标的变异程度计算权重,实现品种的综合评价。最后结合棉花区试资料,介绍了该法的应用,验证了其可行性和有效性。     

Classification of wheat grains by digital image analysis using statistical filters

The aim of pattern recognition by image analysis is to accurately determine the parameters of a given object so that recognition can be achieved. The recognition procedure finds its mathematical tools in signal processing. In the classification of wheats by image processing, spots and dust particles, considered 'noise' in the terminology of signal processing, may significantly decrease the performance of an edge detection algorithm. To remove a pulse noise and to extract the boundary of a geometry, rank-order filters have been proved to yield good results in the processing of an image. The developed software ImPro for Windows employs the median filtering technique to remove such impurities and to smooth out the surface of wheat grains. It also employs the range-order filtering technique to detect boundaries in the classification of wheat varieties. A captured image can be sent to the software directly from a frame grabber. Under standard lighting conditions, boundaries of grains are fit to ellipses, and feature parameters are determined. The parameters determined by fitting procedure, as well as the features determined by direct extraction, are used in the classification. Results show that the dimensionless rational features are useful in distinguishing most of the cultivars and advanced breeding lines tested. This revised version was published online in August 2006 with corrections to the Cover Date.     

The key technology research of on-line surface inspection for hot heavy rail

To overcome the detection difficulties of the surface defects when milling a hot heavy rail,a suite of detection system for on-line surface defect is developed. To select optical environment according to radiation of heavy rail and characteristics of light,use multi-linear array CCD cameras to parallel multi-angle collect images of whole surface of hot heavy rail,and obtain images through an image adaptive preprocessing to meet requirements of defect detection. For the condition that the defects extracting causing by continuity of the surface defects of hot heavy rail is hard to realize through traditional image segmentation, an examining algorithm of image relevance between pixel lines is proposed, using pixel de-differentiation and statistical variance operations to extract defect. This algorithm is demonstrated to be better than traditional edge detection,and the system has achieved good results in the practical application of a rail beam plant.     

Accelerated Generalized Minimal Residual Algorithm

This paper studies the fundamental theory of the generalized minimal residual algorithm(GMRES(m))in Krylov subspace and specially the relationship between residual vector and Krylov subspace.The relationship of the algorithm convergence and the subspace be selected is further researched according the linear system about residual vector.It is posed that the convergence can be slowed down because there are so many very small eigenvalue in magnitude.And a accelerated method(AGMRES(m)) is proposed to improve the convergence of the GMRES(m).Theoretical analysis and numerical results show the reliability and efficiency of the algorithm.     

储粮害虫图像的灰关联分析方法研究

对储粮害虫进行实时监测是非常必要的，其中基于图像识别的检测是目前研究较多的一种方法。本文提出了一种基于灰关联分析的图像边缘检测的新算法，并将其应用于储粮害虫图像的边缘检测，计算表明，该方法能有效地检测粮虫图像的边缘信息。     

Job-Shop scheduling based on improved immune cloning algorithm

Parallel immune clone algorithm is proposed based on population coevolution theory and parallel computing affinity of individual at multiple compute nodes. Introducing the immune memory mechanism, the evolution processes of antibody population and memory units are conducted simultaneously, meanwhile, it improves mutual cooperation among antibodies, and ensures solution set approaching optimal solution from the inside of feasible region or infeasible region border. Clone proliferation, high frequency variation and operation of crossover operators increase the chance that better individuals gain affinity maturation by the operation of clone expansion, improve diversity of antibody population distribution, achieve the balance of optimization between depth and range, and ensure the convergence of the algorithm and the diversity of the search range. A computational study for a standard data set is carried out to test the validity of the algorithm, and the effect of algorithm parameters on the results is analyzed. The simulation results show that the global search capability, local search capability, algorithm stability and computing speed of the algorithm are all superior to conventional optimization algorithms such as normal immune clone optimization algorithm, genetic algorithm, etc.     

多尺度积角点检测和视觉颜色特征的鲁棒车牌定位算法

为解决生鲜产品配送过程中的质量控制问题,将质量功能展开(QFD)与计划-实施-检查-处理(PDCA)循环相结合,运用QFD获得生鲜产品配送质量控制的关键指标,针对关键控制指标,将PDCA循环模型运用到预防措施和纠偏措施的制定和持续改进中,为生鲜产品配送中的质量控制提供指导,从而保证生鲜产品的质量。     

A spread spectrum steganalysis algorithm based on discontinuity detection

A novel audio steganalysis method is proposed.. the audio signal is denoised with wavelet transform. Then, a part of noise signal with different length is intercepted circularly and is used to calculate the cross correlation sequence with the rest of the noise signal. With the wavelet discontinuity detection technique, the feature is extracted from the cross correlation sequence for steganalysis and find out the steg audio. The detection rate is determined by the embedding strength of the secret message other than the embedding capacity. Experimental results show that the more embedding intensity of PN sequence is, the higher the detection rate will be. The detection rate of the algorithm is above 80% when the strength of the PN sequence is about 0.002, which demonstrates that the proposed algorithm has good detection performance.     

Fuzzy webpage text classification algorithm combined with improved NMF

An item-document weight matrix representing the web pages could be generated by constructing the vector space model. Since the efficiency of direct classification through the high-dimensional matrix is relatively low, a fuzzy webpage text classification algorithm combined with improved nonnegative matrix factorization (NMF) is presented. Firstly, the original high-dimensional data are mapped into the low-dimensional semantic space via an iterative normalized compression NMF(NCMF) to reduce the complexity of the problem. Secondly, in order to solve the problem of categorizing ambiguous words by using deterministic matrices, fuzzy logic is incorporated into the classification model, where the fuzzy categorization set of the document is constructed with the fuzzy membership degree between features and categories. Comparative experiment results demonstrate the proposed classification algorithm possesses higher accuracy and better time performance.     

用模糊神经网络提高洪峰预报精度的研究

在大量研究的基础上,提出了基于模糊理论的神经网络改进算法,用来提高对洪峰的预报精度。该方法在网络训练时引入模糊理论来确定网络误差修改的程度。引入的算法增大了大值输出样本和期望输出的误差,使得网络向着提高洪峰拟合精度的方向修改权重。应用表明,改进的模糊BP神经网络能够较好的反映洪水演进机理,提高了神经网络洪水预报模型对洪峰的预报精度,保证了洪峰预报的可靠性。     

改进遗传算法在饲料配方设计中的应用

针对现有饲料配方软件的局限性,以白绒山羊饲料配方为例,提出一种基于改进遗传算法的求解方法。与标准遗传算法相比,采用随机联赛选择替代轮盘赌模型,优化了选择策略;采用不同的随机数交叉和高斯变异,改进了交叉算子与变异算子。测试结果表明,该方法具有良好的运算效率,拓宽了搜索空间,提高了对重点区域的搜索能力,降低了成本,为复杂问题的优化提供了一种新的思路。