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搜索结果: 1-13 共查到particle swarm optimization相关记录13条 . 查询时间(0.102 秒)
A novel particle swarm optimization based approach for the estimation of epipolar geometry for remotely sensed images is proposed and implemented in this work. In stereo vision, epipolar geometry is d...
Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented ...
For a linear array, the excitation coefficients of each element and its geometry play an important role, because they will determine the radiation pattern of the given array. Side Lobe Level (SLL) is ...
In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO), is introduced into close range photogrammetric data processi...
Rational Function Models (RFM) are one of the most considerable approaches for spatial information extraction from satellite images especially where there is no access to the sensor parameters. As the...
In this article we consider a version of the Particle Swarm Optimization (PSO) algorithm which is appropriate for search tasks of multi-agent systems consisting of small robots with limited sensing ca...
Reactive power planning (RPP) involves optimal allocation and determination of the type and size of new reactive power (VAR) supplies to satisfy voltage constraints during normal and contingency state...
In this paper, an optimal and intelligent multi-focus image fusion algorithm is presented, expected to achieve perfect reconstruction or optimal fusion of multi-focus images with high speed. A syner...
The detection and estimation of gravitational wave (GW) signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the si...
为了解决K-均值算法对农业图像中常用的超绿特征2G—R—B图像分割效果不佳的缺点,提出一种基于微粒群与K均值算法的图像分割方法。先用K均值算法对图像进行快速分类,然后将分类结果作为其中一个微粒的结果,利用微粒群算法计算,最后用K-均值算法在新的分类基础上计算新的聚类中心,更新当前的位置,以得到最优的图像分割阈值。试验结果表明,改进算法对超绿特征2G—R—B图像能够准确分割目标,且对不同类型的农业超...
为了增加Pareto集的多样性,提高多目标优化的全局寻优能力,提出了一种基于动态聚集距离的多目标粒子群算法(DCD-MOPSO)。该算法利用改进的快速排序方法来减少计算量,采用动态变化的惯性权重和加速因子以增强算法的全局寻优能力,并基于动态聚集距离对外部集进行维护以增加Pareto集的多样性。通过典型测试函数的仿真实验和应用实例对DCD-MOPSO算法性能进行了分析,并与多目标优化算法MOPSO和...
The optimization of land-use structure is the core of optimizing the allocation of land resources, including the optimization of quantity and space. However, traditional methods such as multi-objectiv...
One of the fundamental steps in the transformation of the LIDAR data into the meaningful objects in urban area involves their segmentation into consistent units through a clustering process. Neverth...

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