基于多判据的多目视觉手势立体姿态检测系统研究

为了精确识别人手姿态用于机器手控制,文中讨论一种基于计算机视觉的手势立体姿态检测的新方法.该方法利用多目视觉之间的配合,有效地解决了检测识别时手部关键节点互相遮档的关键问题.采用了自适应阈值分割、harris角点检测、外极线约束等几种合理、有效的点的匹配算法,对不同摄像机拍摄到的手部关键节点进行排序,这样不仅解决了特征...     

基于场景复杂度计算的红外图像平台直方图均衡

直方图均衡类算法由于缺乏限制项,增强后的红外图像易出现灰度级合并和过增强,视觉效果不佳。为解决上述问题,提出基于场景复杂度限制型的平台直方图均衡算法。该算法首先提出对图像场景复杂度进行估计,并将Harris角点数量作为图像复杂度评价依据。然后建立类似sigmoid函数的图像复杂度评价函数,将场景复杂度标准化。最后综合考虑图像复杂度和防止原图均值漂移,依据平台直方图均衡原理获取全局变换函数,重建红外图像。实验结果表明：该方法可有效地依据不同场景实现红外图像增强,不出现过增强和噪声放大。客观评价指标表明,该方法在保持图像均值亮度和图像信息熵方面表现优异。     

Allocation and sizing of battery energy storage system for primary frequency control based on bio-inspired methods: A case study

This paper presents a new procedure for optimal allocation and optimal sizing of a battery energy storage system (BESS) for primary frequency support in an isolated power system. For the BESS allocation, a transmission bus system with larger frequency decline is recognized and the BESS sizing is performed by a constrained optimization strategy based on a new modified metaheuristic, called Developed Harris Hawks Optimization (DHHO). The simulation results of the suggested DHHO are compared with Bat Optimization Algorithm (BOA) and Genetic Algorithm (GA) from the literature to show the method efficiency. The final results showed higher precision with lower required iterations for the suggested DHHO method. Also, the proposed DHHO gives lower investment costs for BESS with lower power and energy requirement toward the other compared methods.     

基于证据理论的特征点匹配算法

从特征点的检测入手,采用了Harris算子来检测图中的特征点,并且分析各种点匹配的策略（如ZNCC,不变向量测度）从中找出不足。然后把证据理论应用到特征点的匹配当中,构造组合的证据理论框架,并且提出了匹配策略的规则。最后,把双向测试的方法也应用到特征点匹配当中。综合多种点匹配策略,从而得到了一种有效的特征点匹配算法。     

基于MHHO-BP算法的DC-DC电路软故障诊断

针对DC-DC电路软故障诊断中特征提取困难和分类准确率低的问题,提出了一种基于多策略改进哈里斯优化算法-反向传播MHHO-BP)神经网络的故障诊断方法。该方法通过VMD对故障信号进行处理,提取其时域和频域特征作为故障向量,采用MHHO算法优化BP神经网络的权值和阈值,建立DC-DC电路的VMD-MHHO-BP软故障诊断模型。实验结果表明,对于DC-DC电路软故障,该方法相较于鲸鱼优化算法(WOA)和蝴蝶优化算法(BOA)优化BP神经网络,其诊断效果好,准确率高。     

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type. This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture. This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection (HHOGCN-WD) technique for Precision Agriculture. The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture. For image pre-processing, the HHOGCN-WD model utilizes a bilateral normal filter (BNF) for noise removal. In addition, coupled convolutional neural network (CCNet) model is utilized to derive a set of feature vectors. To detect and classify weed, the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance. The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset. The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches, with increased accuracy of 99.13%.     

基于角点匹配的图像拼接技术

采用的Harris角点匹配的算法是基于图像灰度的自相关函数,直接从灰度图像中检测兴趣点的算法.该算法简单,而且配准精度较高,具有较好的抗干扰的能力.此算法使图像拼接过程中对图像重叠律的要求降低到了15%,同时使图像的配准拼接速度提高了60%.达到了兼顾算法的精度和效率两个方面的目的.     

基于自适应神经网络的双摄像机标定

摄像机标定是计算机视觉中非常重要的一环,传统标定方法需要求解内外参数,非常繁琐。通过建立自适应神经网络直接学习图像坐标与空间坐标间的关系。该方法对Harris角点提取结果进行增加约束的改进,从而提高网络训练样本精度,通过程序实现隐层神经元的自适应选取,并综合运用正则化、提前终止策略,使网络的泛化能力得到极大的改善。最后通过与经典标定方法进行对比的实验证明基于自适应神经网络具有很好的双摄像机标定精度。     

基于特征的hausdorff距离图像配准

针对图像配准问题,提出了基于Harris及SIFT(Scale-invariant feature transform)特征的Hausdorff距离方法来实现图像配准。首先利用harris角点检测和SIFT特征提取参考图像和待配准图像的角点,通过两种方法获得的角点在融合之后获得更大的角点搜索范围,再利用相似一致性匹配原则剔除错误角点,进而通过改进的Hausdorff距离算法完成图像的配准操作。结果证明,改进算法比传统Hausdorff距离算法运行时间更短,算法时间降低约45%,具有较强的抗噪声能力和旋转鲁棒性,提高了图像配准的效率和精确性。     

图像局部特征的提取检测技术

对图像进行特征提取和分析,是图像匹配或物体识别的基础,详细介绍了几种目前较为流行的局部特征提取技术,并总结了各种方法的优缺点。