基于改进鲸鱼优化算法的最大2维熵图像分割

为了避免原鲸鱼优化算法早熟收敛、易陷入局部最优等缺陷,首先在原鲸鱼优化算法初始化过程中采用了猫映射产生混沌序列结合反向解方法取代随机产生初始种群;其次在位置更新机制上采用了疯狂算子和黄金正弦算法的方法;最后将改进鲸鱼优化算法用于寻求图像2维最大熵来确定图像分割最佳阈值的选取.对10个经典基准函数进行了试验仿真验证,得到...     

基于双重改进的鲸鱼优化算法

传统的鲸鱼优化算法(WOA)容易陷入局部最优以及收敛速度慢,针对此问题进行研究,提出了一种改进的鲸鱼优化算法,改进算法首先用非线性收敛因子替换原本使用的收敛因子,改进后的非线性收敛因子可以有效利用在算法中以弥补该算法在计算过程中全局探索与局部开发能力中的缺陷,并且可以加快算法收敛速度;然后在鲸鱼位置更新公式中加入了自适应权重,该策略可以改善算法的寻优精度以及进一步提高收敛速度;最后,在固定参数和不同维度的8个基准测试函数上进行了实验,结果表明,改进后的算法在寻找最优位置的精度和收敛速度对比于传统的鲸鱼算法和其他智能优化算法均有着显著的提高,具有更好的优化效果.     

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

Telecommunication Systems - The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the...     

Harmonic optimization of multilevel converters using genetic algorithms

In this letter, a genetic algorithm (GA) optimization technique is applied to determine the switching angles for a cascaded multilevel inverter which eliminates specified higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any number of levels. As an example, in this paper a seven-level inverter is considered, and the optimum switching angles are calculated offline to eliminate the fifth and seventh harmonics. These angles are then used in an experimental setup to validate the results.     

Retinal oximetry based on nonsimultaneous image acquisition using a conventional fundus camera

To measure the retinal arteriole and venule oxygen saturation (SO(2)) using a conventional fundus camera, retinal oximetry based on nonsimultaneous image acquisition was developed and evaluated. Two retinal images were sequentially acquired using a conventional fundus camera with two bandpass filters (568 nm: isobestic, 600 nm: nonisobestic wavelength), one after another, instead of a built-in green filter. The images were registered to compensate for the differences caused by eye movements during the image acquisition. Retinal SO(2) was measured using two wavelength oximetry. To evaluate sensitivity of the proposed method, SO(2) in the arterioles and venules before and after inhalation of 100% O(2) were compared, respectively, in 11 healthy subjects. After inhalation of 100% O(2), SO(2) increased from 96.0 ±6.0% to 98.8% ±7.1% in the arterioles (p=0.002) and from 54.0 ±8.0% to 66.7% ±7.2% in the venules (p=0.005) (paired t-test, n=11). Reproducibility of the method was 2.6% and 5.2% in the arterioles and venules, respectively (average standard deviation of five measurements, n=11).     

基于多层快速多极子算法分析平面多层结构

由于多层平面结构空域闭式格林函数难以用加法定理展开,因此,将多层快速多极子算法推广到该类问题的分析存在一定困难.采用二级离散复镜像技术,获得了多层介质空域格林函数,通过对其表达形式做简单的改写,提出针对多层平面结构的多层快速多极子算法,详细讨论了加法定理展开条件对离散复镜像系数的影响及处理办法.数值算例验证了本文方法的准确性和有效性.     

Non-maximum suppression for object detection based on the chaotic whale optimization algorithm

Non-maximum suppression (NMS) as a post-processing step for object detection is mainly used to remove redundant bounding boxes in the object and plays a vital role in many detectors. Its positioning accuracy mainly depends on the bounding box with the highest score, and this strategy is difficult to eliminate the false positive. In order to solve the problem, this paper regards the post-processing step as a combinatorial optimization problem and combines the chaotic whale optimization algorithm and non-maximum suppression. The chaotic search method is used to generate an initial combinatorial solution, and the whale optimization algorithm is discretized to create an updated combinatorial strategy. Under the guidance of the fitness function, the optimal combination is searched. In addition, the method of difference set area (DSA) is proposed to optimize the final detection result. The experiment uses the current mainstream framework Faster R-CNN as the detector on PASCAL VOC2012, COCO2017 and the Warships datasets. The experimental results show that the proposed method can significantly improve the average precision (AP) of detectors compared with the most advanced methods.     

Evaluation based segmentation algorithm optimization: Idea and system

This paper conies up with a new idea that tries to optimize the performance of image segmentation algorithms based on their objective evaluation knowledge. A prototype expert system is designed and implemented according to this idea. Kxperimental results indicate that this system is feasible and practical. This idea also offers an efficient approach for algorithm optimization of image processing and analysis.     

Retinal blood vessel segmentation using line operators and support vector classification

In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development, we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine. The effectiveness of both methods is demonstrated through receiver operating characteristic analysis on two publicly available databases of color fundus images.     

A fuzzy genetic multiobjective optimization algorithm for a multilevel generalized assignment problem

We consider a task-operator-machine assignment problem where we seek to minimize the total execution time, to come as close as possible to a perfect load balance among the operators and not to exceed neither predefined inter-operator communication costs nor a prefixed number of resources. Besides, in an industrial environment where work force frequently changes, manufacturing systems need to be flexible and critical decisions have to be quickly taken. In this context, a fuzzy genetic multiobjective optimization algorithm is developed to solve a multilevel generalized assignment problem usually encountered in the clothing industry.     

Multispectral image segmentation using the rough-set-initialized EM algorithm

The problem of segmentation of multispectral satellite images is addressed. An integration of rough-set-theoretic knowledge extraction, the Expectation Maximization (EM) algorithm, and minimal spanning tree (MST) clustering is described. EM provides the statistical model of the data and handles the associated measurement and representation uncertainties. Rough-set theory helps in faster convergence and in avoiding the local minima problem, thereby enhancing the performance of EM. For rough-set-theoretic rule generation, each band is discretized using fuzzy-correlation-based gray-level thresholding. MST enables determination of nonconvex clusters. Since this is applied on Gaussians, determined by granules, rather than on the original data points, time required is very low. These features are demonstrated on two IRS-1A four-band images. Comparison with related methods is made in terms of computation time and a cluster quality measure.     

基于区域分割的序列红外图像融合算法

针对传统的基于像素点和窗口策略的融合算法对图像特征表征的失真,提出了一种基于区域分割的序列图像融合算法.首先将序列红外图像分割为3个不同的特征区域,目标区域、背景区域以及灰度区域,并将分割结果映射到可见光图像中.随后,利用多尺度几何分析工具非下采样Contourlet变换(NSCT)有效提取图像特征的特点,根据不同区域的特性在NSCT域设计不同的融合规则.对试验结果进行主观和客观的对比,结果表明:基于区域分割的序列图像融合算法不仅能够为融合图像保留更全面、丰富的背景信息,还能够更加有效、准确地提取图像中的目标特征.该算法优于传统的基于像素点和窗口规则的融合算法,是一种有效可行的图像融合算法.     

基于粒子群优化非局部模糊聚类图像分割算法

为提高局部模糊聚类算法(WFLICM)对噪声图像 分割的抗噪性,克服模糊聚类图像分割算法对初 始聚类中心的敏感性及易陷入局部最优问题,在WFLICM算法的基础上提出一种基于粒子群 优化的融合 局部和非局部空间信息的模糊聚类图像分割算法(PSO-WMNLFCM)。首先,利用粒子群优化 算法的全局 寻优能力得到最优粒子,并以此粒子作为模糊聚类算法的初始聚类中心。其次,用像素的非 局部空间信息 替换模糊因子中的局部邻域值,产生新的目标函数。最后,由拉格朗日乘子法最小化目标函 数,得到隶属 度和聚类中心的更新公式,从而完成图像分割。仿真结果表明,PSO-WMNLFCM算法相比于 模糊局部聚 类(FLICM)算法、局部模糊权重(WFLICM)算法、非局部模糊聚类(NLFCM)算法、非局部模 糊聚类 (MNLFCM)算法、基于粒子 群的局部模糊聚类(PSO-FLICM)算法的划分系数提高了20.92%,20.51%,24.84%,1.44%,23.28%左右。     

Saliency-directed color image segmentation using modified particle swarm optimization

Color image segmentation, an ill-posed problem, can be treated as a process of dividing a color image into some constituent regions and each region is homogeneous. In this study, a saliency-directed color image segmentation approach using “simple” modified particle swarm optimization (PSO) is proposed, in which both low-level features and high-level image semantics extracted from each color image are employed. To extract high-level image semantics from each color image, the visual attention saliency map for each color image is generated by three (color, intensity, and orientation) feature maps, which is used to guide region merging using “simple” modified PSO and a hybrid fitness function for color image segmentation. The proposed approach contains four stages, namely, color quantization, feature extraction, small region elimination, and region merging using “simple” modified PSO. Based on the experimental results obtained in this study, as compared with four comparison approaches, the proposed approach usually provides the better color image segmentation results.     

Array pattern optimization using electromagnetism-like algorithm

This paper proposes a design method of array pattern optimization for sidelobe level (SLL) reduction. The element positions and their weights are determined by electromagnetism-like (EM)-like algorithm. Simulation results show that the EM-like algorithm is efficient in SLL reduction for beamwidth restriction. Synthesis of array pattern using EM-like algorithm is very straightforward and efficient. It does not require any gradient operation and is very easy in both formulation and programming. Therefore, the EM-like algorithm can be applied to many other applications in array signal processing.     

MRF model based image segmentation using hierarchical distributedgenetic algorithm

An unsupervised method for segmenting noisy and blurred images is proposed. A Markov random field (MRF) model is used which is robust to degradation. Since this is computationally intensive, a hierarchical distributed genetic algorithm (HDGA) is used which is unsupervised and parallel. Experimental results show that the proposed method is effective at segmenting real images     

Three-dimensional space-vector modulation algorithm for four-leg multilevel converters using abc coordinates

In this paper, a novel three-dimensional (3-D) space-vector algorithm for four-leg multilevel converters is presented. It can be applied to active power filters or neutral-current compensator applications for mitigating harmonics and zero-sequence components using abc coordinates (referred from now on this paper as natural coordinates). This technique greatly simplifies the selection of the 3-D region where a given voltage vector is supposed to be found. Compared to a three-level modulation algorithm for three-leg multilevel converters, this algorithm does not increase its complexity and the calculations of the active vectors with the corresponding switching time that generate the reference voltage vector. In addition, the low-computational cost of the proposed algorithm is always the same and it is independent of the number of levels of the converter.     

联合多特征提取与内部优化的海陆分离

目前大多数海陆分离方法依靠先验信息或仅利用灰度特征对图像进行处理,对导致的误分割或大量孤立区域无法较好处理,造成后续处理工作难度大。针对这一问题,提出一种联合多特征提取与分离结果优化的海陆分离方法。针对不同的陆地类型提取不同的特征进行分割,对得到的结果采用面积与纹理相结合的方式进行优化,进而得到最终的海陆分离结果。实验结果表明,本文方法与常用的海陆分离方法相比能够快速完整地实现海陆分离并将陆地屏蔽,有利于后续处理工作的进行。     

Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model

In order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally,the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map,avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset,the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well.