层析成像图像重建算法综述

介绍了层析成像技术的图像重建算法,并从正向问题数学模型的简化和反向问题数学模型的映射结构的角度比较了各种算法的特点和优劣。研究表明:用本质是线性算法的各种变换方法重建图像存在严重失真,而卷积滤波的引入可以使变换方法的重建效果有所改善;基于导数搜索的迭代算法对初始值依赖性强、收敛速度慢并且容易陷入局部最优解;基于Fourier变换的方法具有本质的局限性;小波变换则可以同时刻画图像时域和频域的细节特征;有限元法通过重建对象像素的智能划分可以简化正问题的复杂性;而具有物理背景的蒙特卡罗法、模拟退火法、遗传算法、粒子滤波法及神经网络法更适合于复杂且非线性的图像重建;智能化、仿生化、并行化以及各种算法的融合是层析成像图像重建算法的发展趋势。     

Registration and integration algorithm in structured light three-dimensional scanning based on scale-invariant feature matching of multi-source images

Based on the homography between a multi-source image and three-dimensional (3D) measurement points, this letter proposes a novel 3D registration and integration method based on scale-invariant feature matching. The matching relationships of two-dimensional (2D) texture gray images and two-and-a-half- dimensional (2.5D) range images are constructed using the scale-invariant feature transform algorithms. Then, at least three non-collinear 3D measurement points corresponding to image feature points are used to achieve a registration relationship accurately. According to the index of overlapping images and the local 3D border search method, multi-view registration data are rapidly and accurately integrated. Experimental results on real models demonstrate that the algorithm is robust and effective.     

Roulette-wheel selection via stochastic acceptance

Roulette-wheel selection is a frequently used method in genetic and evolutionary algorithms or in modeling of complex networks. Existing routines select one of N individuals using search algorithms of O(N) or O(logN) complexity. We present a simple roulette-wheel selection algorithm, which typically has O(1) complexity and is based on stochastic acceptance instead of searching. We also discuss a hybrid version, which might be suitable for highly heterogeneous weight distributions, found, for example, in some models of complex networks. With minor modifications, the algorithm might also be used for sampling with fitness cut-off at a certain value or for sampling without replacement.     

A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.     

一种无相机标定的立体图像对校正新方法

双目立体视觉是光学被动三维测量的芷要方法。为了快速、准确地寻找对应点,通常要将立体图像对进行校正,消除垂直视差。提出了一种无需标定相机的立体图像对校正方法。该方法从基本矩阵计算初始透视投影、旋转和竖直平移变换矩阵．然后以对应点坐标为基础对这些变换矩阵进行优化计算,从而有效地避免了优化计算的局部最小值,而且不过分依赖基本矩阵的计算精度。通过对提出的方法和完全基于基本矩阵的图像校正方法以及无需基本矩阵计算的图像校正方法进行实验比较,结果表明提出的方法图像校正速度快,能有效地消除垂直视差,而产生的图像变形较小。     

Partial evolution based local adiabatic quantum search

Recently, Zhang and Lu provided a quantum search algorithm based on partial adiabatic evolution, which beats the time bound of local adiabatic search when the number of marked items in the unsorted database is larger than one. Later, they found that the above two adiabatic search algorithms had the same time complexity when there is only one marked item in the database. In the present paper, following the idea of Roland and Cerf [Roland J and Cerf N J 2002 Phys. Rev. A 65 042308], if within the small symmetric evolution interval defined by Zhang et al., a local adiabatic evolution is performed instead of the original “global” one, this “new” algorithm exhibits slightly better performance, although they are progressively equivalent with M increasing. In addition, the proof of the optimality for this partial evolution based local adiabatic search when M=1 is also presented. Two other special cases of the adiabatic algorithm obtained by appropriately tuning the evolution interval of partial adiabatic evolution based quantum search, which are found to have the same phenomenon above, are also discussed.     

Community detection based on modularity and an improved genetic algorithm

Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q$Q$ as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.     

一种基于多准则决策和PSO-LM混合优化算法的多峰Brillouin散射谱的特征提取方法

采用传统方法对多峰Brillouin散射谱进行拟合的过程中,通常是以谱线最大功率点为基准的,却忽略了其他比该点小但却是极值的功率点。这样获得的拟合曲线通常只有一个峰值,相当于把除最高峰之外还有多个小峰的多峰Brillouin散射谱进行了简化,导致大量有用信息的丢失。为了提高Brillouin散射谱的特征提取精度,提出了一种基于MCDM和PSO-LM混合优化算法的多峰Brillouin散射谱特征提取方法(MCDM-PSO-LM)。MCDM可以识别和准确定位多峰Brillouin散射谱的各个波峰和波谷;PSO-LM混合优化算法可以实现分别对各个波峰和波谷的曲线进行拟合并找到每一个波峰的中心频率,该算法既克服了PSO算法过早收敛于局部极值和LM算法依赖初值的问题,又可以将PSO算法的全局搜索能力和LM算法的局部收敛能力结合在一起。较传统算法而言,MCDM-PSO-LM算法保证了对最优值求解的速度和精度,提高了运算能力,使解析解最大限度地接近最优值。分别在不同信噪比和不同线宽条件下进行仿真验证,频移和温度误差分析结果表明,MCDM-PSO-LM方法可以对多峰Brillouin散射谱的各个波峰与波谷进行准确定位,可用于多峰Brillouin散射谱的特征提取,识别效果明显强于传统算法,提高了信息分析的准确性。     

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).     

Hiding solutions in random satisfiability problems: a statistical mechanics approach

A major problem in evaluating stochastic local search algorithms for NP-complete problems is the need for a systematic generation of hard test instances having previously known properties of the optimal solutions. On the basis of statistical mechanics results, we propose random generators of hard and satisfiable instances for the 3-satisfiability problem. The design of the hardest problem instances is based on the existence of a first order ferromagnetic phase transition and the glassy nature of excited states. The analytical predictions are corroborated by numerical results obtained from complete as well as stochastic local algorithms.     

一种高光谱图像波段选择的快速混合搜索算法

由于高光谱图像的高数据维和大数据量,现有的波段选择方法大多不能同时具有良好的效果和较短的计算时间。提出了一种用于高光谱图像波段选择的新方法——快速混合搜索算法。该算法将全局搜索和局部寻优有机的结合起来,能够在较短的时间内获得最佳的波段组合,用于高光谱图像的目标分类识别。快速混合搜索算法克服了传统搜索方法在高光谱图像波段选择中的缺陷,能够在提高所选波段性能的同时节省大量的运算时间。分别利用200波段和126波段的AVIRIS对其数据进行了仿真实验。实验结果表明,快速混合搜索算法在所选波段性能和计算耗时方面都获得了令人满意的效果。     

A new interface tracking method: The polygonal area mapping method

We present a new method, the polygonal area mapping (PAM) method, for tracking a non-diffusive, immiscible material interface between two materials in two-dimensional incompressible flows. This method represents material areas explicitly as piecewise polygons, traces characteristic points on polygon boundaries along pathlines and calculates new material areas inside interface cells via polygon-clippings in a discrete manner. The new method has very little spatial numerical diffusion and tracks the interface singularities naturally and accurately. In addition to high accuracy, the PAM method can be directly used on either a structured rectangular mesh or an unstructured mesh without any modifications. The mass conservation is enforced by heuristic algorithms adjusting the volume of material polygons. The results from a set of widely used benchmark tests show that the PAM method is superior to existing volume-of-fluid (VOF) methods.     

蛋白质晶体结构刚体优化的新方法

将多元函数的变尺度法与两电子密度图的相关系数相结合,得到一种能够对蛋白质晶体结构进行刚体优化的新方法.初步测试结果表明,该方法能够明显地改善待测晶体的初始模型在晶胞中的取向和位置.而与最大似然方法相比,该方法可能更适用于搜索空间中存在大量局域极值点的情况. 关键词： 蛋白质晶体结构 刚体优化 多元函数的变尺度方法 电子密度图的相关系数     

基于SRLG不相关的共享保护算法的研究

通过对共享保护算法的深入分析,使用K条最短路和迭代思想的方法,提出了两种共享风险链路组不相关的共享保护算法,并在仿真平台上对两种算法的性能进行了仿真.KWFF算法借鉴了传统的K条最短路策略,并且在每一个波长平面上,都对新到业务进行了K条工作路由的计算,极大挖掘了网络中潜在的波长资源.而IFF算法由于引入了迭代的思想,避免了共享风险链路组问题中,所特别有“陷阱”问题的出现,并且利用两套权重计算公式,在计算工作路由和保护路由的时候,充分考虑了网络资源的实时变化情况.通过仿真数据可以看到,与以往算法相比,KWFF和IFF算法大大降低了网络阻塞率,并且提高了网络资源的使用效率.     

Experimental implementation of local adiabatic evolution algorithms by an NMR quantum information processor

Quantum adiabatic algorithm is a method of solving computational problems by evolving the ground state of a slowly varying Hamiltonian. The technique uses evolution of the ground state of a slowly varying Hamiltonian to reach the required output state. In some cases, such as the adiabatic versions of Grover's search algorithm and Deutsch-Jozsa algorithm, applying the global adiabatic evolution yields a complexity similar to their classical algorithms. However, using the local adiabatic evolution, the algorithms given by J. Roland and N.J. Cerf for Grover's search [J. Roland, N.J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65 (2002) 042308] and by Saurya Das, Randy Kobes, and Gabor Kunstatter for the Deutsch-Jozsa algorithm [S. Das, R. Kobes, G. Kunstatter, Adiabatic quantum computation and Deutsh's algorithm, Phys. Rev. A 65 (2002) 062301], yield a complexity of order N (where N=2(n) and n is the number of qubits). In this paper, we report the experimental implementation of these local adiabatic evolution algorithms on a 2-qubit quantum information processor, by Nuclear Magnetic Resonance.     

A non-rigid cardiac image registration method based on an optical flow model

According to non-rigid medical image registration, new method of classification registration is proposed. First, Feature points are extracted based on SIFT (Scale Invariant Feature Transform) from reference images and floating images to match feature points. And the coarse registration is performed using the least square method. Then the precise registration is achieved using the optical flow model algorithm. SIFT algorithm is based on local image features that are with good scale, rotation and illumination invariance. Optical flow algorithm does not extract features and use the image gray information directly, and its registration speed is faster. The both algorithms are complementary. SIFT algorithm is used for improving the convergence speed of optical flow algorithm, and optical flow algorithm makes the registration result more accurate. The experimental results prove that the algorithm can improve the accuracy of the non-rigid medical image registration and enhance the convergence speed. Therefore, the algorithm has some advantages in the image registration.     

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。