Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Medical image enhancement is an essential process for superior disease diagnosis as well as for detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However, speckle noise corrupts the CT images and makes the clinical data analysis ambiguous. Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using log transform in an optimization framework. In order to achieve optimization, a well-known meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal parameter settings for log transform. The performance of the proposed technique is studied on a low contrast CT image dataset. Besides this, the results clearly show that the CS based approach has superior convergence and fitness values compared to PSO as the CS converge faster that proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness of the proposed enhancement technique.

一种基于杜鹃搜索算法的图像自适应增强方法

图像增强是图像处理中关键步骤,基于归一化的非完全Beta函数变换的图像增强具有理想的增强效果。然而合理选取归一化的非完全Beta函数的参数是算法的关键和难点,常需要人工干预或是计算非常耗时。杜鹃搜索算法是一种新型的仿生智能算法,具有自适应、自组织等智能特性,具有强大的寻找优化解的能力。这里将杜鹃搜索算法用于归一化的非完全Beta函数参数的自适应选取,实现了基于杜鹃搜索算法的归一化的非完全Beta函数图像增强方法,实际图像增强实验结果表明了该方法的有效性和可行性。

低照度图像自适应颜色校正与对比度增强算法

光照变化会造成图像颜色失真和清晰度的下降,为了使计算机视觉系统具有颜色恒常感知功能,提出了一种基于极限学习机和杜鹃搜索算法的图像颜色校正与对比度增强算法.首先对于输入图像,提取该图像的17维特征向量,并利用训练好的极限学习机神经网络自适应地选择适合该图像的最佳颜色恒常算法,并进行相应的颜色校正;然后,针对图像的亮度分量,利用杜鹃搜索算法自动确定亮度增强函数的最优参数,并进行相应的对比度增强.基于Funt数据集的实验结果表明,文中算法不仅能有效地完成图像颜色校正,还能自适应地提高图像的信息量和对比度,获得图像颜色和对比度的综合最佳视觉质量.

自适应直方图均衡化的合成孔径雷达图像增强

合成孔径雷达(Synthetic Aperture Radar,SAR)成像技术在海岸线检测、舰船检测等领域具有广泛应用。为了解决SAR图像质量不稳定的问题,提出一种自适应直方图均衡化的SAR图像增强方法。该方法通过均值漂移聚类方法删除图像中低信息量的数据,以缓解局部暗淡现象与噪声放大的现象。设计了增强的布谷鸟搜索算法学习图像直方图均衡化的最优参数,实现自适应的SAR图像增强处理。在SAR图像上完成了验证实验,结果表明,该增强方法使SAR图像在主观视觉评价与客观定量评价上均取得了明显提高。

布谷鸟搜索的非均匀弱光图像自适应增强算法

为了解决现有图像增强算法容易产生光晕伪影以及细节丢失的问题,文中针对非均匀弱光条件下的图像提出一种新的增强算法。通过色彩空间变换提取原始彩色图像的亮度Y分量,采用引导滤波增强图像细节;对细节增强后的亮度图像采用改进的Sigmoid函数(S型曲线)进行自适应线性增强,其中改进的Sigmoid函数最佳参数的获取是以布谷鸟搜索算法寻得的使图像对比度最大时的参数作为最佳参数;通过线性色彩转化得到增强后的彩色图像。实验结果表明,该算法对非均匀弱光图像有明显的增强效果,在峰值信噪比、结构相似度、对比度质量指数及绝对平均亮度误差四个评价指标中优于其他增强算法,各指标平均提高约41.6%、26.0%、61.3%、63.3%,可有效地改善非均匀弱光图像在增强过程中所存在的增强不足以及光晕伪影现象。

基于布谷鸟搜索优化的红外热像仪对比度增强方法

由于探测器原理性的缺陷,红外热像仪所获图像的对比度低、细节不明显。对比度受限的自适应直方图均衡化方法(Contrast Limited Adaptive Histogram Equalization,CLAHE)能够增强红外图像的对比度,但是其受限参数无法适应不同的场景,且需要手动调整。作为一种智能种群优化算法,布谷鸟搜索算法具备强大的寻优能力。基于该算法,使CLAHE方法在不同场景下获取最优参数,进而实现红外图像对比度的场景自适应增强。实验分析表明,这种改进的算法具有可行性和适应性。