Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm

In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.

2-D mini mumfuzzy entropy method of image thresholding based on genetic algorithm

A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

Two-Dimensional Entropy Method Based on Genetic Algorithm

Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The proposed method uses both the gray value of a pixel and the local average gray value of an image. At the same time, the simple genetic algorithm is improved by using better reproduction and crossover operators. Thus the proposed method makes up the 2 D entropy method’s drawback of being time consuming, and yields satisfactory segmentation results. Experimental results show that the proposed method can save computational time when it provides good quality segmentation.

A New Adaptive Image Segmentation Method

Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

Fast recursive algorithm for two-dimensional Tsallis entropy thresholding method

Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.

月球探测器鲁棒环形山检测及光学导航方法

针对月球探测器环形山检测方法受光照影响、鲁棒性差的问题,本文提出一种基于极大熵阈值三值化的鲁棒环形山检测算法。采用不同滤波核对图像进行去噪平滑,然后对处理后的图像进行极大熵阈值分割、将图像信息三值化,去除图像对光源的敏感性,同时最大程度保留图像信息;提出一种归一化多指标约束环形山匹配和拟合方法完成环形山提取,将环形山提取算法应用于光学导航中进行打靶实验验证算法实时性表现。仿真结果表明:与传统基于形态学或自适应边缘检测的方法相比,本文方法在较大尺度条件下提取出连续、光滑的环形山边缘,有效环形山数量提升35%以上,同时实时性更好、计算消耗降低40%;基于鲁棒环形山提取的光学导航算法实时性更好。

熵最优与改进SCA的图像分割及其图像识别应用

针对传统图像分割效率低、精度差的不足,提出一种混合变异正余弦算法的多阈值图像分割方法。为提高SCA算法的寻优性能,设计拉丁超立方种群初始化改进种群多样性;以非线性转换因子动态调节算法搜索能力;融入惯性权重机制提升算法全局寻优;结合高斯和拉普拉斯分布混合变异对个体扰动,使算法跳离局部最优。将Cross熵作为适应度函数,利用HMSCA求解分割阈值。实验结果表明,该算法可以提升图像分割精度和效率。将其应用于火灾图像识别,能够实现火焰源与背景分离,得到更好的分割效果。

基于灰狼自适应阈值分割和改进模糊增强的红外图像NSCT增强算法

研究低成本和便携的红外成像技术是最近几年带电检测的发展趋势,为减少红外检测环境、红外传感器以及其他因素的影响,解决红外检测中红外图像含噪声干扰、模糊和对比度低的问题,文章设计了一种基于灰狼自适应阈值分割和改进模糊增强的红外图像NSCT增强算法。对原始红外图像进行NSCT域变换;变换后含有噪声的高频分量采用VT去噪后,接着采用改进模糊增强处理;对变换后含有电力设备主体的低频分量进行灰狼自适应阈值分割为背景和前景部分,随后分别进行增强处理;最后将处理后的各分量进行逆NSCT变换。经对比应用,验证了该算法应用在变电站电力设备红外检测上的优越性:文章算法与其他算法相比在边缘强度、信息熵、对比度、标准差、峰值信噪比五类评价指标上的涨幅至少为3.94%、2.16%、9.86%、7.45%、21.86%。文章算法处理后的红外图像符合人眼视觉效果,更易于人眼识别故障,有利于电力设备热故障的检测与故障定位。

改进?鱼优化算法和熵测度的图像多阈值分割

针对传统图像多阈值分割方法存在效率低、分割质量差等问题,提出一种改进?鱼优化算法并结合熵测度(weight lens remora optimization algorithm,WLROA)的图像多阈值分割方法。针对?鱼优化算法易陷入局部极值等缺陷,引入透镜成像反向学习策略,生成透镜反向解来增加种群多样性,进而提高算法跳出局部极值能力;提出一种自适应权重因子,对个体位置进行自适应扰动,提高算法探索能力。以最小化交叉熵作为优化目标,利用WLROA确定最小交叉熵并获得相应分割阈值。选取部分伯克利大学分割数据集图像和遥感图像测试提出算法的分割性能,测试结果表明,WLROA与其他知名算法相比具有更好的分割效果,能够有效实现复杂图像的精确处理。

Multilevel Image Thresholding Using Tsallis Entropy and Cooperative Pigeon-inspired Optimization Bionic Algorithm

Multilevel thresholding is a simple and effective method in numerous image segmentation applications.In this paper,we propose a new multilevel thresholding method that uses cooperative pigeon-inspired optimization algorithm with dynamic distance threshold(CPIOD)for boosting applicability and the practicality of the optimum thresholding techniques.Firstly,we employ the cooperative be havior in the map and compass operator of the pigeon-inspired optimization algorithm to overcome the"curse of dimensionality"and help the algorithm converge fast.Then,a distance threshold is added to maintain the diversity of the pigeon population and increase the vitality to avoid local optimization.Tsallis entropy is used as the objective function to evaluate the optimum thresholds for the considered gray scale images.Four benchmark images are applied to test the property and the stability of the proposed CPIOD algorithm and three other optimization algorithms in multilevel thresholding problems.Segmentation results of four optimization algorithms show that CPIOD algorithm can not only get higher quality segmentation results,but also has better stability.

基于改进蜉蝣算法优化多阈值图像分割

针对图像多阈值分割中存在分割效率低、计算时间长以及精度不高等问题,提出了一种基于改进蜉蝣算法的多阈值图像分割算法。首先,在初始化阶段引入类随机采样方法中的Sobol序列,增强种群的遍历性和多样性;其次,提出一种自适应非线性惯性权重,平衡了全局与局部寻优能力,提高了算法的收敛效率,利于种群向最优解逼近;最后,采用指数熵作为计算适应度的目标函数,通过改进蜉蝣算法对图像分割的多阈值组合进行寻优,确定最优分割阈值。为了验证该改进算法的有效性,选择了伯克利图像来进行分割验证,并与其他智能算法进行比较。实验结果表明:该改进算法在分割准确性、计算时间、结构衡量指标(structure similarity index measure,SSIM)和峰值信噪比(peak signal-to-noise ratio,PSNR)上均优于对比算法,能快速有效地解决复杂多目标图像的多阈值分割问题,具有较强的工程实用性。

双馈风机变换器开路故障不完全检修策略研究

为了解决故障检修过程中容易出现开路故障再次发生的问题,提出一种基于改进最大熵算法的双馈风机变换器开路故障不完全检修策略。利用红外热成像仪采集双馈风机变换器状态红外热图像,并经过滤波和增强处理提高图像细节的识别度。创新性地通过改进最大熵算法确定最佳分割阈值,对图像的目标和背景进行分割,选取目标部分进行故障检修。采用卷积神经网络提取图像特征并降维。利用分类器估算每个故障类别的可能性,实现双馈风机变换器开路故障不完全检修。研究结果表明:所提策略可以准确地对双馈风机变换器开路故障进行检修,得到的检修准确率、精确度、召回率和F1值分别为0.8、1.0、1.0和0.86。与现有策略相比,所提策略具有较高的检修准确性和可靠性,可以解决故障检修过程中开路故障再次发生的问题。

基于遗传算法的图像阈值选取

阈值分割是图像分割中的一种常用的方法,传统的OTSU阈值化方法对信噪比较低的图像分割效果不理想,而二维最大类间方差法充分利用像素与领域的空间相关信息,能够产生较为满意的分割结果,但运算量大大增加,文中采用遗传算法,通过编码、选择、交叉、变异等操作对二维最大类间方差法进行了优化,试验结果表明,遗传算法可以大大提高运算速度,对算法的优化效果十分明显。

改进的最大熵阈值分割及其快速实现

针对传统二维最大熵阈值法对二维直方图采用近似处理等的不足,提出了改进的二维最大熵快速阈值分割方法。首先对邻域模板进行改进,将改进后的模板用来构建二维直方图,并将最大熵法用于此直方图上,以便获得最佳阈值;然后,舍弃传统的二维直方图中关于主对角区域的概率近似为1的假设,使阈值选取更准确;最后,分析二维直方图投影,得到其特性,并证明两定理的存在。利用此特性和两定理导出新型、快速的递推算法来降低计算复杂度。仿真实验结果表明,与当前二维最大熵法相比,提出的方法不仅分割更准确和抗噪性更强,而且占用的存储空间更少,分割速度更快,分割时间少于0.04s。

基于最大熵的灰度阈值选取方法

图像分割是图像处理中的一个重要问题。在最大类间方差法和一致性准则法的基础上,运用最大熵原理来选择灰度阈值对图像进行分割。实验结果表明,本算法确定的阈值具有更佳的分割效果。

基于二维阈值化和遗传算法的图像分割方法

本文以二维最大熵为例,讨论了如何利用遗传算法和二维直方图进行图像阈值的自动选取。实验结果表明,利用遗传算法可以有效地提高原有图像阈值选取方法的速度。

一种改进的基于最大类间方差的图像分割方法

在分析最大类间方差阈值图像分割算法原理的基础上,根据实际图像直方图中目标与背景的像素分布特点,提出了一种改进的最大类间方差图像分割算法。该算法充分考虑了图像直方图中目标与背景存在交叉的情况,通过与传统阈值分割算法的实验比较,证明该算法具有分割精度高、效果好、速度快的特点。

基于二维最大相关准则的图像阈值分割

提出了基于二维最大相关准则的自动阈值图像分割算法.该方法根据图像的二维直方图中目标和背景分布的相关量最大来选择阈值,能够实现比传统最大相关准则更强的抗噪声能力.同时将遗传算法用于对二维最大相关准则阈值分割的优化,试验结果表明该算法可以实现快速、准确图像分割.

最优进化图像阈值分割算法

针对图像阈值分割问题,根据遗传算法理论提出最优进化图像阈值分割算法.将图像中每个像素点看作一个染色体,阈值看作进化方向,假设最优进化方向存在,建立进化方向更新模型;然后定义了染色体编码规则,通过简单随机采样进行种群初始化,重新定义了适值函数和选择机制,在适当的交叉率和变异率下得到最优阈值;同时分析了假设和模型的合理性.实验结果表明,文中的假设和进化方向更新模型合理,该算法是稳定、有效的图像阈值分割算法.