Hybrid bio-inspired lateral inhibition and Imperialist Competitive Algorithm for complicated image matching

Bio-inspired intelligent algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been applied to solve image matching problems. However, due to high computational complexity and premature convergence problems associated with these methods, they have limitations in defining the global optimal matcher efficiently and accurately. To address these problems, we proposed a hybrid bio-inspired optimization approach, coupling the lateral inhibition mechanism and Imperialist Competitive Algorithm (ICA), to solve complicated image matching problems. With the adoption of the lateral inhibition mechanism, the global convergence of conventional ICA algorithms has been greatly improved. We demonstrate the efficiency and feasibility of the proposed approach by extensive comparative experiments.     

Chaotic biogeography-based optimization approach to target detection in UAV surveillance

This paper describes a novel chaotic biogeography-based optimization (CBBO) algorithm for target detection by means of template matching to meet the request of unmanned aerial vehicle (UAV) surveillance. Template matching has been widely applied in movement tracking and other fields and makes excellent performances in visual navigation. Biogeography-based optimization (BBO) algorithm emerges as a new kind of optimization method on the basis of biogeography concept. The idea of migration and mutation strategy of species in BBO contributes to solving optimization problems. Our work adds chaotic searching strategy into BBO and applies CBBO in template matching. By utilizing chaotic strategy, the population ergodicity and global searching ability are improved, thus avoiding local optimal solutions during evolution. Applying the algorithm to resolving template matching problem overcomes the defects of common image matching. Series of experimental results demonstrate the feasibility and effectiveness of our modified approach over other algorithms in solving template matching problems. Our modified BBO algorithm performs better in terms of convergence property and robustness when compared with basic BBO.     

Cauchy Biogeography-Based Optimization based on lateral inhibition for image matching

In this paper, a hybrid method of Cauchy Biogeography-Based Optimization (CBBO) and Lateral Inhibition (LI) is proposed to complete the task of complicated image matching. Lateral inhibition mechanism is adopted for image pre-process to make the intensity gradient in the image contrastively strengthened. Biogeography-Based Optimization (BBO) is a bio-inspired algorithm for global optimization which is based on the science of biogeography, searching for the global optimum mainly through two steps: migration and mutation. To promote the optimization performance, an improved version of the BBO method using Cauchy mutation operator is proposed. Cauchy mutation operator enhances the exploration ability of the algorithm and improves the diversity of population. The proposed LI-CBBO method for image matching inherits both the advantages of CBBO and lateral inhibition mechanism. Series of comparative experiments using Particle Swarm Optimization (PSO), LI-PSO, BBO and LI-BBO have been conducted to demonstrate the feasibility and effectiveness of the proposed LI-CBBO.     

Truss optimization on shape and sizing with frequency constraints based on orthogonal multi-gravitational search algorithm

Structural optimization on shape and sizing with frequency constraints is well-known as a highly nonlinear dynamic optimization problem with several local optimum solutions. Hence, efficient optimization algorithms should be utilized to solve this problem. In this study, orthogonal multi-gravitational search algorithm (OMGSA) as a meta-heuristic algorithm is introduced to solve truss optimization on shape and sizing with frequency constraints. The OMGSA is a hybrid approach based on a combination of multi-gravitational search algorithm (multi-GSA) and an orthogonal crossover (OC). In multi-GSA, the population is split into several sub-populations. Then, each sub-population is independently evaluated by an improved gravitational search algorithm (IGSA). Furthermore, the OC is used in the proposed OMGSA in order to find and exploit the global solution in the search space. The capability of OMGSA is demonstrated through six benchmark examples. Numerical results show that the proposed OMGSA outperform the other optimization techniques.     

一种多视点视频自动颜色校正系统

针对多视点视频系统中视点间图像颜色不一致的问题,提出了一种多视点视频自动颜色校正系统。通过求取目标图像和源图像间的颜色校正矩阵,判断其是否满足全局校正的要求;对不满足要求的图像,通过图像分割和K-L变换(Karhunen-Loeve transform),建立起目标图像和源图像中各分割区域间的局部映射关系,并通过感兴趣区域匹配,来实现对源图像的校正,最后通过视频跟踪技术实现对视频图像的校正。以标准的多视点测试图像集为例,通过将新方法与直方图匹配、全局一维线性校正算法等进行比较,表明新方法能消除匹配失真的影响,且具有较好的颜色校正效果。研究结果表明该系统可以很好地揭示图像间的颜色变化关系,并且具有很好的内容自适应性,是一种有效的多视点视频图像系统颜色校正方法。     

基于联合正则化及压缩传感的MRI图像重构

基于压缩传感的MRI图像重构利用图像稀疏的先验知识能从很少的投影值重构原图像。目前MRI重构算法只利用MRI图像稀疏性表示或只利用基于其局部光滑性的先验知识,重构效果不理想。针对此问题,结合两种先验知识,提出一种基于联合正则化及压缩传感的MRI图像重构方法。利用块坐标下降法将求解联合正则化问题转化为交替求解二次凸优化、稀疏正则化和全变差正则化三个简单的优化问题。并提出分别采用共轭梯度法、二元自适应收缩法以及梯度下降法对以上优化问题求解。实验结果表明,该算法重构效果比现有算法有明显地提高。     

基于粒子群优化算法的自适应图像分割方法

提出了一种基于粒子群优化算法的图像分割新方法。粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域缩短了寻找阈值的时间。将PSO用于基于改进的最佳加权熵阈值法的图像分割中,试验结果表明,该方法不仅能够避免陷入局部极值,而且其速度得到了明显的改善,是一种有效的图像分割新方法。     

基于粒子群优化的核匹配追踪目标识别

提出了一种基于粒子群优化的用于目标识别的核匹配追踪算法.该算法用粒子群优化算法在基函数字典中选择最优的基函数,大大降低了基匹配追踪算法的计算复杂度.通过与标准核匹配追踪算法及基于遗传算法的核匹配追踪算法对UCI数据集及纹理图像的识别试验表明,核匹配追踪算法优良的分类性能以及粒子群优化算法高效的全局搜索能力使新算法能有效识别目标数据.     

基于时间序列预测的电子稳像算法研究

块匹配电子稳像算法是一种稳定性好、准确度高的电子稳像算法.块匹配算法在目标区域中从起始点到匹配点进行搜索时,需要对图像块进行反复匹配,计算量大、实时性差成为限制其应用的主要问题.本文从缩小块匹配算法搜索范围的思想出发,提出了一种利用时间序列预测来确定最优搜索起始点的电子稳像算法.根据图像序列全局运动矢量的内部统计特性,选择合适的时间序列模型;采用AIC准则和Durbin-Levinson递推算法估计模型的阶次和参量,并通过残差检验对模型进行检验和更新.利用建立的时间序列模型和历史数据对当前时刻全局运动矢量进行最优预测,并将其作为搜索起点来进行下一步精确搜索.实验结果证明,时间序列预测方法有效缩小了块匹配算法的搜索范围,使计算速度得到较大幅度的提高,并可直接推广到其它电子稳像算法中.     

Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation

Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton-Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement.     

利用图像分割的基于图割的立体匹配算法

立体匹配通过寻找同一空间景物在不同视点下投影图像的像素间的一一对应关系, 最终得到该景物的视差图。在对匹配算法作了深入研究的基础上, 提出了一种利用图像分割的基于图割的立体匹配算法。算法把参考图分割成多个区域, 然后用平面公式在一个分割中建立视差。视差模板是从初始视差分割中提取的。每一个分割被分配到精确的视差模板。构建全局能量函数,能量函数的鲁棒最小化是由基于图割的最优化获得的。算法对低纹理区域和接近视差边界区域有很好的匹配效果, 同时, 又解决了传统的基于全局算法中计算量过大, 实时性不好的问题。实验表明, 本算法能满足高精度、高实时性要求。     

EGO方法的训练算法及应用

针对高效全局优化(Efficient Global Optimization,简称EGO)方法的训练问题,选择元启发式(Meta-heuristic)算法、随机取样算法以及低频序列算法,并选用三个无约束、两个带约束解析优化算例以及两个气动优化算例,对这三类训练算法进行详细地比较研究,发现在元启发式算法中差分进化算法最具应用潜力,而低频序列算法可以有效降低EGO方法的随机性,其中Faure序列平均性能最优.     

一种改进的红外图像归一化互相关匹配算法

分析了传统归一化互相关算法在红外空中目标匹配定位时失效的原因,提出一种改进的红外图像归一化互相关匹配算法.该方法将模板和匹配区域之间的纹理相关计算看作一个最优化问题,寻求使图像纹理相关匹配鲁棒性最好的相关基准值,用图像的相关基准函数替代传统方法中的区域均值部分,构造了一种适用于的红外目标匹配的归一化相关算法.实验结果表明,该相关匹配算法对模板中背景部分的变化和非均匀性亮度变化有良好的抗干扰能力,较好地解决了恶劣环境下红外对空目标跟踪中匹配定位出错的问题.     

一种基于单元信息熵的目标匹配改进算法

为解决图像信息熵无法有效进行图像匹配的问题,将图像单元信息熵和投影特征相结合,定义了图像单元信息熵,并提出了一种基于单元投影信息熵的图像匹配方法.在单元信息熵的基础上,在各个单元格内进行单元信息熵投影计算,然后按照一定的测度进行计算,从而实现图像的匹配.采用网格分层的搜索算法,加快搜索速度,提高其工程实用性.实验证明:该算法具有良好的抗几何失真能力和抗辐射失真的能力,以及很好的抗噪声干扰的能力,可以准确的进行目标匹配.     

基于云模型的自适应量子免疫克隆算法

利用云模型能够兼顾随机性和模糊性的品质,提出一种基于云模型的自适应量子免疫克隆算法.使用云算子代替通用的量子旋转门这一量子进化算法核心算子用于寻优变异操作;通过控制云算子间的协作,实现算法在进化过程中对搜索范围的动态调整,使算法具有较强的全局搜索能力;同时,补充针对性的优化方案,有效避免了算法陷入局部最优.对标准数值优化问题的仿真对比实验表明,该算法具有寻优能力强、搜索精度高、稳定度好等优点;对非线性系统的参数估计仿真实验,该算法也取得了对参数的高精度有效估计.     

Acceleration of Global Optimization Algorithm by Detecting Local Extrema Based on Machine Learning

This paper features the study of global optimization problems and numerical methods of their solution. Such problems are computationally expensive since the objective function can be multi-extremal, nondifferentiable, and, as a rule, given in the form of a “black box”. This study used a deterministic algorithm for finding the global extremum. This algorithm is based neither on the concept of multistart, nor nature-inspired algorithms. The article provides computational rules of the one-dimensional algorithm and the nested optimization scheme which could be applied for solving multidimensional problems. Please note that the solution complexity of global optimization problems essentially depends on the presence of multiple local extrema. In this paper, we apply machine learning methods to identify regions of attraction of local minima. The use of local optimization algorithms in the selected regions can significantly accelerate the convergence of global search as it could reduce the number of search trials in the vicinity of local minima. The results of computational experiments carried out on several hundred global optimization problems of different dimensionalities presented in the paper confirm the effect of accelerated convergence (in terms of the number of search trials required to solve a problem with a given accuracy).     

Hyperspectral unmixing based on ISOMAP and spatial information

Several nonlinear techniques have recently been proposed for classification and unmixing applications in hyperspectral image processing. A commonly used data-driven approach for treating nonlinear problems employs the geodesic distances on the data manifold as the property of interest. Although this approach often produces better results than linear unmixing algorithms, the graph-based method treats an image as a bag of spectral signatures and ignores the relationship between the pixel and its spatial neighbors. To utilize the spatial distribution of pixels and improve hyperspectral unmixing precision effectively, a new method is proposed for incorporating nonlinear dimension reduction and spatial information, using isometric mapping (ISOMAP) to find significant low-dimensional structures hidden in high-dimensional hyperspectral data. Spatial information is also introduced into the traditional spectral-based endmember search process. A fully constrained least-squares algorithm is used to evaluate the abundance of each endmember. The experimental results for actual images reveal that the performance of the proposed method obtains much better unmixing results than the classical N-FINDR and ISOMAP algorithms.     

A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics

In this study, a design of integrated computational intelligent paradigm has been presented for numerical treatment of the one-dimensional boundary value problems represented with Falkner-Skan equations (FSE) by exploitation of Gaussian wavelet neural networks (GWNNs), genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., GWNN-GA-SQP. The GWNNs is used for mathematical modeling of the problem by constructing mean squared error based objective function while optimization of the cost function is initially conducted with efficacy of GAs as a global search and while fine tuning is performed with efficiency local search with SQP. The numerical results are obtained by proposed GWNN-GA-SQP for different FSEs arising in nonlinear regimes of computation fluid mechanics studies. A comparison of the results of proposed GWNN-GA-SQP stochastic numerical solver with reference state of the art solutions of Adams method establishes the accuracy, convergence and stability, which further endorsed through statistics on multiples runs. The T-Paired test is also applied to validate the effectiveness of the proposed GWNN-GA-SQP algorithm for solving nonlinear FSEs.     

Corner detection and matching for infrared image based on double ring mask and adaptive SUSAN algorithm

Infrared (IR) image fusion is designed to fuse several IR images into a comprehensive image to boost imaging quality and reduce redundancy information, and image matching is an indispensable step. However, Conventional matching techniques are susceptible to the noise and fuzzy edges in IR images and it is therefore very desirable to have a matching algorithm that is tolerant to them. This paper presents a method for infrared image matching based on the SUSAN corner detection. To solve the problems of the traditional SUSAN algorithm including the fixed threshold of gray value difference and the failed detection of symmetry corners, an adaptive threshold extraction method is raised in this study. Furthermore, an attached double ring mask is used to improve the complex corner detection capability. A constraint condition and a principle of gravity are adopted to filtrate the candidate corners. The proposed method is qualitatively and quantitatively evaluated on IR images in the experiments. In comparison with other methods, better performance has been achieved.     

基于特征点及优化理论的图像自动拼接方法

提出了一种新的图像拼接方法,首先利用相位一致性(phase congruency)算法进行特征点检测,利用本文提出的匹配点优选策略进行特征点对自动选取,然后用LM(Levenberg-Marquardt)算法进一步优化变换矩阵,最后对拼接结果进行融合处理,获得无缝拼接的图像.该方法把基于特征点和基于优化理论的拼接方法有效相结合,且能充分利用图像重叠部分的信息,在一定程度上克服了噪声及光照不均的影响,较传统方法具有更强的鲁棒性和更高的拼接精确度.试验结果证明了该方法的有效性.