针对目前视频拼接技术中的主要问题,即SURF(Speed Up Robust Features)特征提取算法与FLANN(Fast Library or Approximate Nearest Neighbors)特征匹配算法在综采工作面恶劣环境中存在特征点误提取和特征点匹配正确率低的问题,提出一种...针对目前视频拼接技术中的主要问题,即SURF(Speed Up Robust Features)特征提取算法与FLANN(Fast Library or Approximate Nearest Neighbors)特征匹配算法在综采工作面恶劣环境中存在特征点误提取和特征点匹配正确率低的问题,提出一种改进SURF-FLANN的综采工作面视频拼接特征提取与匹配算法。为了提高特征点提取正确率,该方法通过将传统的高斯滤波换为更为先进的双边滤波提取图像中的SURF关键特征点,同时在特征向量中引入特征点4-领域内的特征点描述符信息,从而改进了描述符算子,进一步提高了特征点的描述能力。为了提升特征点匹配速度,提出了R-FLANN(Random Sample Consensus-Fast Library or Approximate Nearest Neighbors)特征匹配算法,该算法利用RANSAC算法获取特征点的匹配先验信息剔除无匹配、误匹配的特征点,从而提高特征点匹配速度。为了验证改进效果,通过消融试验验证了改进SURF-FLANN的特征提取与匹配算法有效提升综采工作面视频图像特征提取和匹配正确率。通过本文方法与SIFT+FLANN,Hairrs与SURF+FLANN的特征提取与匹配算法进行特征点提取与匹配的对比试验,结果表明本文方法特征提取与匹配平均正确率和平均匹配速度最高,分别达到了81.47%和51.47帧/s。通过运用本文方法与SIFT+FLANN,Hairrs与SURF+FLANN的特征提取与匹配算法进行视频图像拼接对比试验,结果表明本文提出的方法在拼接效果清晰度、对比度、熵、拼接速率指标都最好,得到了最佳效果。展开更多
Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-ins...Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical Things.It has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of patients.Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results.Since the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection approach.However,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each other.This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational complexity.The proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.展开更多
Cognitive-inspired computational systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals.It also helps in early and consistent decision-making for various ...Cognitive-inspired computational systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals.It also helps in early and consistent decision-making for various health issues including human psychological health.Water fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also provide a relaxing environment to the visitors.These natural sounds have a direct effect on the psychological health of visitors.Very few research works are reported on developing the relationship between water sounds and their corresponding psychological impact.This assessment needs trained manpower and a lot of experimental time which is costly and may not be always available.In this paper,to access the from the pleasantness from human health-friendly water fountain sounds,a perceptually weighted functional link artificial neural network(P-FLANN)model is developed.To reduce the computational complexity of training and for faster convergence,swam intelligence-based optimization algorithm is used for updating the weights.It is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95%accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.展开更多
针对传统图像识别算法匹配正确率低、运行时间较长等问题,文中提出了基于改进ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)的工件图像识别方法。对ORB算法特征描述、图像特征匹配算法...针对传统图像识别算法匹配正确率低、运行时间较长等问题,文中提出了基于改进ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)的工件图像识别方法。对ORB算法特征描述、图像特征匹配算法进行修改,解决传统图像识别算法在图像存在尺度和旋转变换情况下存在的弊端并降低误匹配率。该方法对ORB算法检测到的特征点采用SURF(Speeded Up Robust Features)算法添加方向信息并完成特征描述,得到旋转尺度不变性的特征点,结合FLANN算法并引入双向匹配策略进行特征点粗匹配,最后利用渐进采样一致算法进一步剔除误匹配点对完成精匹配。实验结果表明,与其他方法相比,改进算法在处理尺度、旋转等变换图像时,匹配正确率分别提高了2.6%~18.8%和29.5%~43.9%,运行时长均在4 s以内,提高了对工件图像的识别效率和精准性。展开更多
在基于偶极子磁场分量梯度的水下磁异常定位方法中,三轴磁力计自身误差及两磁场坐标系配准误差等是限制水下定位精度的主要因素,因此有必要对其进行校正,补偿磁场分量梯度计测量值。建立了磁场分量梯度计的测量误差模型,提出了基于函数...在基于偶极子磁场分量梯度的水下磁异常定位方法中,三轴磁力计自身误差及两磁场坐标系配准误差等是限制水下定位精度的主要因素,因此有必要对其进行校正,补偿磁场分量梯度计测量值。建立了磁场分量梯度计的测量误差模型,提出了基于函数链接型神经网络(functional link artificial neural network,FLANN)和最小二乘法的磁场分量梯度计误差校正方法,给出了误差参数辨识及校正算法,数值仿真和实测数据证明了校正算法具有良好的收敛性,能显著地抑制磁场分量梯度测量误差,该校正方法为提高磁场分量梯度计性能提供了一种可行途径。展开更多
文摘Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical Things.It has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of patients.Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results.Since the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection approach.However,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each other.This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational complexity.The proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.
文摘Cognitive-inspired computational systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals.It also helps in early and consistent decision-making for various health issues including human psychological health.Water fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also provide a relaxing environment to the visitors.These natural sounds have a direct effect on the psychological health of visitors.Very few research works are reported on developing the relationship between water sounds and their corresponding psychological impact.This assessment needs trained manpower and a lot of experimental time which is costly and may not be always available.In this paper,to access the from the pleasantness from human health-friendly water fountain sounds,a perceptually weighted functional link artificial neural network(P-FLANN)model is developed.To reduce the computational complexity of training and for faster convergence,swam intelligence-based optimization algorithm is used for updating the weights.It is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95%accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.
文摘针对传统图像识别算法匹配正确率低、运行时间较长等问题,文中提出了基于改进ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)的工件图像识别方法。对ORB算法特征描述、图像特征匹配算法进行修改,解决传统图像识别算法在图像存在尺度和旋转变换情况下存在的弊端并降低误匹配率。该方法对ORB算法检测到的特征点采用SURF(Speeded Up Robust Features)算法添加方向信息并完成特征描述,得到旋转尺度不变性的特征点,结合FLANN算法并引入双向匹配策略进行特征点粗匹配,最后利用渐进采样一致算法进一步剔除误匹配点对完成精匹配。实验结果表明,与其他方法相比,改进算法在处理尺度、旋转等变换图像时,匹配正确率分别提高了2.6%~18.8%和29.5%~43.9%,运行时长均在4 s以内,提高了对工件图像的识别效率和精准性。
文摘在基于偶极子磁场分量梯度的水下磁异常定位方法中,三轴磁力计自身误差及两磁场坐标系配准误差等是限制水下定位精度的主要因素,因此有必要对其进行校正,补偿磁场分量梯度计测量值。建立了磁场分量梯度计的测量误差模型,提出了基于函数链接型神经网络(functional link artificial neural network,FLANN)和最小二乘法的磁场分量梯度计误差校正方法,给出了误差参数辨识及校正算法,数值仿真和实测数据证明了校正算法具有良好的收敛性,能显著地抑制磁场分量梯度测量误差,该校正方法为提高磁场分量梯度计性能提供了一种可行途径。
