基于直方图预处理与BF算法的含噪图像分割

图像中的噪声会直接影响图像分割质量,为快速、准确地识别含噪图像中的目标,提出一种基于直方图预处理与BF算法的含噪图像分割方法。该方法通过小波变换抑制图像中的噪声,分析增强图像的直方图特点以缩小分割阈值的分布范围,以二维最大类间方差为原则设计分割目标函数,利用BF算法快速搜索最优分割阈值。实验结果表明,该方法在收敛速度、稳定性和分割效果三个方面均优于基于遗传算法、人工鱼群算法等其他群体智能的分割方法。     

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。     

基于灰色Tsallis熵的SAR图像快速分割*

针对SAR图像斑点噪声及分割速度慢的问题,提出一种基于灰色理论和Tsallis熵的SAR图像快速分割方法。该方法首先对待分割图像进行小波变换,将表征图像概貌信息的低频部分重构为概貌图像,表征图像细节和边缘的高频部分重构为细节图像,并建立了相应的概貌—细节共生矩阵模型;然后利用灰色理论和Tsallis熵设计了基于该共生矩阵的灰色Tsallis熵模型,用于求解最优分割阈值;同时,为加快阈值搜索速度,引入群体智能中的粒子群优化算法。实验结果显示,新方法在抗噪性、分割速度和灵活性三个方面均有明显提高。     

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

Multilevel thresholding is one of the most popular image segmentation techniques. In order to determine the thresholds, most methods use the histogram of the image. This paper proposes multilevel thresholding for histogram-based image segmentation using modified bacterial foraging (MBF) algorithm. To improve the global searching ability and convergence speed of the bacterial foraging algorithm, the best bacteria among all the chemotactic steps are passed to the subsequent generations. The optimal thresholds are found by maximizing Kapur's (entropy criterion) and Otsu's (between-class variance) thresholding functions using MBF algorithm. The superiority of the proposed algorithm is demonstrated by considering fourteen benchmark images and compared with other existing approaches namely bacterial foraging (BF) algorithm, particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques. Experimental results show that the Otsu based optimization method converges quickly as compared with Kapur's method.     

An improved biogeography based optimization approach for segmentation of human head CT-scan images employing fuzzy entropy

The present paper proposes the development of a three-level thresholding based image segmentation technique for real images obtained from CT scanning of a human head. The proposed method utilizes maximization of fuzzy entropy to determine the optimal thresholds. The optimization problem is solved by employing a very recently proposed population-based optimization technique, called biogeography based optimization (BBO) technique. In this work we have proposed some improvements over the basic BBO technique to implement nonlinear variation of immigration rate and emigration rate with number of species in a habitat. The proposed improved BBO based algorithm and the basic BBO algorithm are implemented for segmentation of fifteen real CT image slices. The results show that the proposed improved BBO variants could perform better than the basic BBO technique as well as genetic algorithm (GA) and particle swarm optimization (PSO) based segmentation of the same images using the principle of maximization of fuzzy entropy.     

改进的鸡群优化算法及其在DTI-FA图像配准中的应用

鸡群优化算法(Chicken Swarm Optimization,CSO)是一个全新的群智能优化算法,简单且具有良好的扩展性。针对鸡群优化算法中因为母鸡的寻优能力差而使算法容易陷入局部极值的问题,提出了一种结合混沌思想的改进鸡群优化算法(Chaotic Improved Chicken Swarm Optimization Algorithm,CICSO)。该算法结合混沌思想的遍历性初始化鸡群位置,将母鸡的位置更新公式改为仅向全局适应度值最好的公鸡学习,并引入学习系数来避免陷入局部最优。最后将改进的鸡群优化算法(CICSO)应用于DTI-FA图像配准。仿真实验结果表明,在解决高维问题时,改进的鸡群优化算法避免了陷入局部极值,提高了收敛精度,在DTI-FA图像配准中提高了图像的配准精确度。     

基于PSO算法的图像分割方法

针对大多数图像分割方法计算量大、不利于实时处理的缺点,提出用微粒群算法（PSO）优化最小误差分割方法.该方法不但具备最小误差分割法受目标和噪声影响小以及对小图像分割效果好的优点,还克服了遗传算法等加速算法需要预先设定众多运行参数,受目标变化影响大的问题.图像分割的效果和速度得到了提高,性能也更加稳定.实验结果反映了该方法的有效性.     

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。     

改进的2维Otsu法及混沌粒子群递推的阈值分割

鉴于现常用的灰度级-平均灰度级2维直方图区域划分将部分目标和背景点错分成边缘和噪声点这一不足,为此提出了一种基于灰度级-梯度2维直方图的Otsu阈值选取新方法,利用混沌粒子群优化算法来寻找分割阈值,并提出在迭代过程中,采用递推方法来大大减少适应度函数的重复计算。实验结果表明,与最近提出的基于灰度级-平均灰度级2维直方图Otsu法及粒子群的快速图像分割方法相比,该新方法由于尽可能地考虑了所有目标点和背景点,从而使分割后的图像区域内部均匀、边界形状准确、特征细节清晰,同时运行时间几乎不到现有算法的1/3,而且粒子群处理的收敛精度得到了进一步提高。     

一种改进彩色图像分割方法

文章首先通过非线性变换把RGB彩色图像转换为HSV彩色图像,然后和构造的一个卷积模板相乘,将相乘结果用于文中提出的改进粒子群优化算法中,将图像分割出来,从而文章提出了一种新的彩色图像分割方法。经过仿真实验表明,文中提出的方法不仅图像分割效果好,而且运算时间也有明显减少,改进粒子群优化算法收敛快且稳定,从而证明了该方法是可行和有效的。     

基于粒子群优化算法和模糊熵的多级阈值图像分割算法

针对现有阈值分割算法利用穷举搜索寻找最优阈值而造成的计算成本较大的问题,提出了一种基于粒子群优化算法和模糊熵的多级阈值图像分割算法。图像分割是图像分析中非常重要的预处理步骤,在提出的方法中,首先选择香农熵和模糊熵作为优化技术的目标函数;然后建立一种基于粒子群优化算法的多层次图像阈值分割,通过最大化香农熵或模糊熵进行图像分割。最后从图像分割数据库中选取Lena、baboon和airplane作为测试图像进行性能分析（包括鲁棒性、效率和收敛性）,并与现有的几种阈值分割算法进行比较。结果显示,提出的算法得到了更高PSNR值和更少的分类误差,证明了该算法是一种高效的多级阈值图像分割算法。     

基于鱼群算法优化normalized cut的彩色图像分割方法

为了克服传统的谱聚类算法求解normalized cut彩色图像分割时,分割效果差、算法复杂度高的缺点,提出了一种基于鱼群算法优化normalized cut的彩色图像分割方法.先对图像进行模糊C-均值聚类预处理,然后用鱼群优化算法替代谱聚类算法求解Ncut的最小值,最后通过最优个体鱼得到分割结果.实验表明,该方法耗时少,且分割效果好.     

An image segmentation method using automatic threshold based on improved genetic selecting algorithm

In this paper, an image segmentation method using automatic threshold based on improved genetic selecting algorithm is presented. Optimal threshold for image segmentation is converted into an optimization problem in this new method. In order to achieve good effects for image segmentation, the optimal threshold is solved by using optimizing efficiency of improved genetic selecting algorithm that can achieve a global optimum. The genetic selecting algorithm is optimized by using simulated annealing temperature parameters to achieve appropriate selective pressures. Encoding, crossover, mutation operator and other parameters of genetic selecting algorithm are improved moderately in this method. It can overcome the shortcomings of the existing image segmentation methods, which only consider pixel gray value without considering spatial features and large computational complexity of these algorithms. Experiment results show that the new algorithm greatly reduces the optimization time, enhances the anti-noise performance of image segmentation, and improves the efficiency of image segmentation. Experimental results also show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximately, and the target region is less than the background region. Therefore, the new method can facilitate subsequent processing for computer vision, and can be applied to realtime image segmentation.     

基于仿生学的优化算法及其在信号处理中的应用

仿生学优化算法是一类模仿生物行为和自然界现象的仿生算法,其目的是求解优化问题的全局最优解。本文首先介绍了各种仿生学优化算法的起源和基本原理,主要包括蚁群优化算法、粒子群优化算法、细菌觅食优化算法、蜂群优化算法、鱼群优化算法、萤火虫群优化算法、狼群优化算法、蝙蝠算法、鸡群优化算法、进化算法、免疫算法、克隆选择算法和小世界网络等。然后总结了仿生优化算法的研究现状,并给出了仿生优化算法在信号处理、图像处理、语音处理和通信网络等领域中的典型应用。最后,归纳了仿生学优化算法的特点,并对如何扩展其适用范围、探索新的仿生学优化算法提出了基本思路,对其发展进行了展望。     

基于DCIWPSO在谷底边缘泡沫图像分割的应用*

矿物浮选过程中,为了预测矿物品位,需要提取大量泡沫图像特征参数,其中泡沫大小是十分重要的图像特征参数。图像分割就是把泡沫图像分割成若干气泡区域的处理技术。谷底边缘分割算法是泡沫分割中一种重要的算法,其中分割阈值是非常重要的量,标准粒子群算法对阈值计算容易陷入局部最优值,难以计算全局最优值,采用改进的粒子群算法,动态改变粒子群中的惯性权重值来得到适合边缘分割的阈值,达到了正确分割泡沫图像的目的。     

A clustering algorithm with optimized multiscale spatial texture information: application to SAR image segmentation

An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.     

改进萤火虫优化算法的Renyi熵污油图像分割

针对传统Renyi熵方法在分割污油图像时存在图片差距大、无法根据不同图片进行最优分割的问题,提出改进萤火虫算法对二维Renyi熵分割算法中的α值进行寻优来解决上述问题。分析了采集的污油图片特点以及对污油图片进行分割的必要性;针对多目标寻优精度不高和后期收敛速度较慢的问题,对萤火虫算法进行了改进,并对初始萤火虫位置进行混沌优化处理,使结果达到全局最优;利用基于改进萤火虫算法的Renyi熵图像分割算法对采集的污油图片进行阈值分割实验,并与二维Renyi熵分割、粒子群算法(PSO)Renyi熵分割方法进行比较。实验结果表明:本文提出的算法可以有效地对污油区域进行分割,能够快速地实现复杂图像的精确处理。     

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

     

改进的红外图像2维Otsu分割算法

针对红外图像有别于一般灰度图像的特点,常用的灰度级平均灰度级2维直方图区域划分在红外图像分割中效果不佳,为此提出一种改进的灰度级梯度2维Otsu阈值选取方法,选取合适的梯度算子,利用改进的粒子群优化算法寻找分割阈值,在算法中加入有效判断早熟停滞的方法,一旦检索到早熟迹象,便随机改变最优解的任意1维分量值,使其跳出局部最大,实现全局寻优过程的快速收敛。仿真实验结果表明,该算法由于使用新的2维直方图,分割后的红外图像边界形状准确,特征细节清晰,运算速度也得到了有效提高。     

基于分水岭和改进的模糊聚类图像分割*

针对模糊C-均值聚类算法需预先给出初始聚类中心、未考虑邻城信息、计算复杂度高等缺点,提出了一种基于分水岭和改进的模糊聚类图像分割方法.该方法首先利用分水岭分割方法对原图像进行预分割,然后利用粒子群的全局寻优能力从预分割的小区域中搜索出较为准确的初始聚类中心;最后,在对小区域进行模糊聚类时,建立了包含邻域信息的聚类目标函...