基于改进粒子群优化的模糊熵煤尘图像分割

针对基本粒子群算法易陷入局部最优和过早收敛的缺陷,提出权重因子自适应的粒子群算法,并对部分粒子进行Morlet变异操作,由此得到改进粒子群优化算法.将该算法和模糊熵相结合并用于图像分割,利用改进粒子群优化算法来搜索使模糊熵最大的参数值,得到模糊参数的最优组合,进而确定图像的分割阈值.通过与其他两种粒子群算法的分割结果进行比较,该算法取得了令人满意的分割结果,且算法运算时间较小,满足煤尘浓度实时精确测量的要求.     

基于粒子群优化算法的阈值图像分割研究

研究将群体智能中的粒子群优化算法应用到图像分割中,提出了一种新的图像分割算法.新方法基于最佳熵阈值分割技术,用粒子群优化算法自适应选取分割阈值.仿真实验针对Lena图像分割问题,将遗传算法与粒子群优化算法分别独立运行,对得到的阈值以及均值、方差进行了比较,并将运行时间作为算法复杂度的评价指标.统计结果显示,论文算法不仅能够对图像进行准确的分割,而且运行时间明显较短.仿真结果表明,基于粒子群优化的图像分割算法是可行的、有效的.     

基于粒子群优化算法的最佳熵阈值图像分割

图像分割是自动目标识别的关键和首要步骤。群智能作为一类新兴的演化计算技术已被越来越多的研究者关注。论文研究将群智能中的粒子群优化算法应用到图像分割中,提出了一种新的图像分割算法。新方法基于最佳熵阈值分割技术,用粒子群优化算法自适应选取分割阈值,基于Bayes定理和随机状态转移过程对新算法收敛性的分析表明,新方法能以概率1找到图像的最佳熵阈值。在仿真实验中,针对基准图像和SAR图像分割问题,将遗传算法与粒子群优化算法分别独立运行10次,对10次得到的阈值以及均值、方差进行了比较,并将运行时间作为算法复杂度的评价指标。统计结果显示,论文算法不仅能够对图像进行准确的分割,而且运行时间明显较短。仿真结果表明,基于粒子群优化的图像分割算法是可行的、有效的。     

基于改进PSO的三维Tsallis熵图像分割

针对二维Tsallis熵图像分割不精确以及优化图像阈值分割函数的元启发式优化算法容易陷入局部最优这两个问题,提出了一种新的三维Tsallis熵阈值分割法以及一种新的改进粒子群优化算法。通过引入均值、中值、梯度三种因素,构建出三维直方图,并结合Tsallis熵理论提出了一种三维Tsallis熵阈值分割法。为了避免粒子群优化算法陷入局部最优,通过引入综合学习策略并改进粒子群优化算法的迭代方式,提出了综合学习改进粒子群优化算法。将提出的三维Tsallis熵阈值分割法与综合学习改进粒子群优化算法结合进行图像分割。与其他元启发式算法相比,综合学习改进粒子群优化算法能在低维环境下有效避免局部最优。实验结果表明相比于二维Tsallis熵阈值分割法,三维Tsallis熵阈值分割法分割效果更好,且具有更好的抗噪性能。由此可以表明综合学习改进粒子群优化算法结合三维Tsallis熵进行图像分割可以取得更好的结果。     

NSCT变换与改进PSO算法在含噪图像分割中的应用

为提高含噪图像的分割效果和分割速度,将非下采样Contourlet变换和粒子群优化算法相结合,提出了一种有效的图像分割方法——IPSOC。该方法首先对待分割图像进行多尺度非下采样Contourlet变换,然后利用其最高级低频系数重构图像,计算重构图像与其均值图像的二维直方图中类间离散度矩阵的迹,并以之作为分割图像的目标函数来搜索最佳分割阈值。为加快阈值搜索速度,以改进的粒子群优化算法作为阈值分割的并行搜索策略,通过对基本粒子群优化算法进行个体及全局最优信息的实时更新,防止粒子停滞操作和阈值保持次数限定搜索终止条件等几个方面的改进,快速有效地获得分割图像。实验结果表明,该方法与基于遗传算法和人工鱼群算法的分割方法相比,明显提高了图像分割速度和分割质量。     

多目标粒子群和人工蜂群混合优化的阈值图像分割算法

在图像分割中,为了准确地把目标和背景分离出来,提出了一种基于多目标粒子群和人工蜂群混合优化的阈值图像分割算法。在多目标优化的框架下,将改进的类间方差准则和最大熵准则作为适应度函数,通过粒子群和蜂群混合优化这2个适应度函数来获得1组非支配解。同时,为了提高全局和局部搜索能力,在蜂群进化时,将粒子群的全局最优解引入到人工蜂群算法的雇佣蜂阶段蜜源的更新中,并对搜索方程进行改进。最后通过类间差异和改进的类内差异的加权比值,从一组非支配解中选取最优阈值。实验结果表明,该算法能够取得理想的分割结果。     

基于小波变异粒子群和模糊熵的图像分割

基于粒子群和模糊熵的图像分割算法用于各种图像分割时,由于基本粒子群算法存在易陷入局部最优以及过早收敛的缺点,使得该算法难以得到理想的分割效果。针对此问题,提出了一种基于小波变异粒子群和模糊熵的图像分割算法,利用小波变异粒子群来搜索使模糊熵最大的参数值,得到模糊参数的最优组合,进而确定图像的分割阈值。通过与其他两种粒子群算法的分割结果进行比较,表明该算法取得了令人满意的分割结果,算法运算时间较小,具有很好的自适应性。     

基于小生境粒子群算法的图像分割方法

为了得到分割图像的最佳阈值,提出了一种基于小生境粒子群算法的图像分割方法。小生境粒子群算法通过划分小生境的方法,保持了物种的多样性,克服了粒子群算法容易陷入局部解,后期收敛速度慢的缺点,提高了算法的全局寻优能力。该方法基于最大类间方差阈值分割技术,用小生境粒子群算法对适应度函数进行优化,得到最佳阈值,并用该阈值对图像进行分割。实验结果表明,与最大类间方差法,基于基本粒子群算法的最大类间方差分割法相比,所提出的方法不仅能得到理想的分割结果,而且分割速度也得到了提高。     

基于二进制量子微粒群算法的二维熵图像分割

分析量子计算的特点,对量子旋转门进行研究,给出了新的量子旋转门调整策略,并与离散二进制粒子群优化算法进行组合,提出了二进制量子粒子群优化算法。该算法具有收敛速度快、全局寻优能力强的特点。用典型复杂函数对其进行测试,测试结果表明,算法的优化质量和效率都优于离散二进制粒子群优化算法。将二进制量子粒子群优化算法与阈值法相结合应用于图像分割,结果表明了基于二进制量子粒子群优化算法的二维熵图像分割法用于阈值寻优具有更快的收敛速度和更好的全局寻优能力。     

基于混沌粒子群算法的多阈值图像分割

针对单阈值图像分割方法在求取比较复杂的图像时效果不理想及粒子群算法容易陷入局部最优且速度较慢等等问题,提出了基于混沌粒子群优化算法的多阈值图像分割方法。该方法利用混沌运动随机性、遍历性和初值敏感性,将混沌粒子群优化算法与多阈值法相结合作全局搜索,实验结果表明了基于混沌粒子群优化算法的多阈值图像分割法用于阈值寻优减少了搜索时间,并且运行时间不随阈值数目的增加而显著增加。     

A hybrid differential evolution for optimal multilevel image thresholding

Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold’s selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the cuckoo search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two parameters adjusted, it is by far a better choice for real-life applications.     

An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix

Image thresholding is a process that separates particular object within an image from their background. An optimal thresholding technique can be taken as a single objective optimization task, where computation and obtaining a solution can become inefficient, especially at higher threshold levels. In this paper, a new and efficient color image multilevel thresholding approach is presented to perform image segmentation by exploiting the correlation among gray levels. The proposed method incorporates gray-level co-occurrence matrix (GLCM) and cuckoo search (CS) in order to effectively enhance the optimal multilevel thresholding of colored natural and satellite images exhibiting complex background and non-uniformities in illumination and features. The experimental results are presented in terms of mean square error (MSE), peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), computational time (CPU time in seconds), and optimal threshold values for each primary color component at different thresholding levels for each of the test images. In addition, experiments are also conducted on the Berkeley Segmentation Dataset (BSDS300), and four performance indices of image segmentation- Probability Rand Index (PRI), Variation of Information (VoI), Global Consistency Error (GCE), and Boundary Displacement Error (BDE) are tested. To evaluate the performance of proposed algorithm, other optimization algorithm such as artificial bee colony (ABC), bacterial foraging optimization (BFO), and firefly algorithm (FA) are compared using GLCM as an objective function. Moreover, to show the effectiveness of proposed method, the results are compared to existing context sensitive multilevel segmentation techniques based on Tsalli's entropy. Experimental results showed the superiority of proposed technique in terms of better segmentation results with increased number of thresholds.     

Fuzzy entropy based optimal thresholding using bat algorithm

Image segmentation is a very significant process in image analysis. Much effort based on thresholding has been made on this field as it is simple and intuitive, commonly used thresholding approaches are to optimize a criterion such as between-class variance or entropy for seeking appropriate threshold values. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. This paper considers image thresholding as a constrained optimization problem and optimal thresholds for 1-level or multi-level thresholding in an image are acquired by maximizing the fuzzy entropy via a newly proposed bat algorithm. The optimal thresholding is achieved through the convergence of bat algorithm. The proposed method has been tested on some natural and infrared images. The results are compared with the fuzzy entropy based methods that are optimized by artificial bee colony algorithm (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO); moreover, they are also compared with thresholding methods based on criteria of between-class variance and Kapur's entropy optimized by bat algorithm. It is demonstrated that the proposed method is robust, adaptive, encouraging on the score of CPU time and exhibits the better performance than other methods involved in the paper in terms of objective function values.     

基于超像素和光流引导的正射影像拼接方法

影像拼接是生成大规模数字正射影像的关键技术之一,但现有的影像拼接方法在进行多个影像拼接时存在拼接线穿过明显地物导致的鬼影现象。光流是观察者和场景间相对运动引起的影像边缘等的相对运动,其中,大光流对应影像间的变化区域,可用于检测正射影像间的明显地面区域。提出一种基于光流引导的新型影像拼接方法,通过超像素的密集光流提取影像中明显的地物信息,以避免接缝穿过明显的地面物体。采用由粗到细的接缝线优化策略,并在超像素级别上利用Dijkstra算法进行最佳拼接区域检测,从而提高接缝线检测的效率。在此基础上,结合归一化互相关成本函数在像素级别上进行拼接线的像素级优化,获得最优的接缝线。实验结果表明,该方法从主观视觉上能够生成高质量的接缝线,在保证拼接效率的情况下,SSIM质量评价指标较Dijkstra方法、图割方法以及商业软件OrthoVista得到明显提高。     

基于粒子群优化算法和改进的Snake模型的图像分割算法

基于活动轮廓（Snake）模型的目标轮廓提取是图像分割中一种重要的方法.为了克服传统Snake模型在图像分割中不能向凹处收敛和收敛不准确的缺点,提出了一种粒子群优化算法与改进的Snake模型相结合的图像分割算法.改进的Snake模型,即在传统的Snake 模型的基础上增加了一个向心能量,增加此能量可以使初始化曲线向目标的凹处收敛.又由于粒子群优化算法具有获得全局最优的能力,可以使曲线能更准确地收敛到目标的边界.通过实验证明此方法可以取得很好的分割效果.     

多阈值模糊互信息图像分割方法

提出了多阈值模糊互信息图像分割新方法。该方法首先将模糊隶属度函数引入到传统互信息量中并定义模糊信息量概念;其次将模糊互信息量用于图像分割时给出具体隶属度函数的构造;最后采用混沌优化法来获得多阈值模糊互信息分割方法的最佳阈值。实验结果表明,提出的多阈值模糊互信息图像分割方法是有效的。     

图像的模糊增强与聚类分割

本文对于被噪声污染而又要求按若干个灰度级作多区域分割的图象处理问题，给出了模糊增强的具体实现过程；并提出基于直方图对多个亮度进行聚类分析，通过建立聚类目标函数的最小化数据，确立多级灰度门限，从而实现图像的多级灰度的最佳分割。     

Multi-level image thresholding using Otsu and chaotic bat algorithm

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu’s between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

     

基于微粒群和最大模糊熵的图像分割

提出了一种基于微粒群和最大模糊熵的图像分割方法.将图像分为目标和背景,并分别建立相应的模糊隶属函数来描述图像各个灰度级属于目标和背景的模糊特性,进而给出图像模糊熵的描述.在此基础上,根据最大模糊熵准则采用微粒群算法搜索模糊参数的最优组合,确定区分目标和背景的最佳阈值.为了验证方法的有效性,对比进行了图像分割实验,并与双峰法、迭代法和最大类间方差法进行了比较,实验结果表明,效果良好,能够自动、有效地选取阈值,分割效果优于其它三种算法,具有很好的鲁棒性和自适应性.     

A reinforcement agent for threshold fusion

Finding an optimal threshold in order to segment digital images is a difficult task in image processing. Numerous approaches to image thresholding already exist in the literature. In this work, a reinforced threshold fusion for image binarization is introduced which aggregates existing thresholding techniques. The reinforcement agent learns the optimal weights for different thresholds and segments the image globally. A fuzzy reward function is employed to measure object similarities between the binarized image and the original gray-level image, and provide feedback to the agent. The experiments show that promising improvement can be obtained. Three well-established thresholding techniques are combined by the reinforcement agent and the results are compared using error measurements based on ground-truth images.