This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors.Usually,the existent descriptors such...This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors.Usually,the existent descriptors such as speeded up robust features(SURF),Kaze,binary robust invariant scalable keypoints(BRISK),features from accelerated segment test(FAST),and oriented FAST and rotated BRIEF(ORB)can competently detect,describe,and match images in the presence of some artifacts such as blur,compression,and illumination.However,the performance and reliability of these descriptors decrease for some imaging variations such as point of view,zoom(scale),and rotation.The intro-duced description method improves image matching in the event of such distor-tions.It utilizes a contourlet-based detector to detect the strongest key points within a specified window size.The selected key points and their neighbors con-trol the size and orientation of the surrounding regions,which are mapped on rec-tangular shapes using polar transformation.The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation.Consequently,the descriptor obtained is invariant(translation,rotation,and scale)because of the two methods;the extraction of the region and the polar transformation techniques used in this paper.The descrip-tion method introduced in this article is tested against well-established and well-known descriptors,such as SURF,Kaze,BRISK,FAST,and ORB,techniques using the standard OXFORD dataset.The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.展开更多
在室内停车场中应用基于RFID的LANDMARC算法进行车辆定位时,由于室内停车场的复杂结构以及多径效应的影响,车辆定位精度不能通过增加参考标签数目或均匀规则的部署参考标签等方式来提升。提出了一种基于虚拟RFID标签的室内定位算法(loca...在室内停车场中应用基于RFID的LANDMARC算法进行车辆定位时,由于室内停车场的复杂结构以及多径效应的影响,车辆定位精度不能通过增加参考标签数目或均匀规则的部署参考标签等方式来提升。提出了一种基于虚拟RFID标签的室内定位算法(location algorithm based on virtual tag,LAVT)。该算法通过近邻标签确定车辆的近邻区域,计算出近邻区域的外心并插入虚拟参考标签;通过虚拟参考标签替换原近邻标签、缩小近邻区域面积,使新近邻标签更临近待定位车辆,从而更精确地计算出车辆的位置。仿真实验表明:LAVT算法在室内停车场环境中将车辆定位精度提升了19.03%。LAVT算法应用于室内停车场环境中的车辆定位具有更好的适用性,能满足室内停车场车辆定位的基本需求。展开更多
文摘This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors.Usually,the existent descriptors such as speeded up robust features(SURF),Kaze,binary robust invariant scalable keypoints(BRISK),features from accelerated segment test(FAST),and oriented FAST and rotated BRIEF(ORB)can competently detect,describe,and match images in the presence of some artifacts such as blur,compression,and illumination.However,the performance and reliability of these descriptors decrease for some imaging variations such as point of view,zoom(scale),and rotation.The intro-duced description method improves image matching in the event of such distor-tions.It utilizes a contourlet-based detector to detect the strongest key points within a specified window size.The selected key points and their neighbors con-trol the size and orientation of the surrounding regions,which are mapped on rec-tangular shapes using polar transformation.The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation.Consequently,the descriptor obtained is invariant(translation,rotation,and scale)because of the two methods;the extraction of the region and the polar transformation techniques used in this paper.The descrip-tion method introduced in this article is tested against well-established and well-known descriptors,such as SURF,Kaze,BRISK,FAST,and ORB,techniques using the standard OXFORD dataset.The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.
文摘在室内停车场中应用基于RFID的LANDMARC算法进行车辆定位时,由于室内停车场的复杂结构以及多径效应的影响,车辆定位精度不能通过增加参考标签数目或均匀规则的部署参考标签等方式来提升。提出了一种基于虚拟RFID标签的室内定位算法(location algorithm based on virtual tag,LAVT)。该算法通过近邻标签确定车辆的近邻区域,计算出近邻区域的外心并插入虚拟参考标签;通过虚拟参考标签替换原近邻标签、缩小近邻区域面积,使新近邻标签更临近待定位车辆,从而更精确地计算出车辆的位置。仿真实验表明:LAVT算法在室内停车场环境中将车辆定位精度提升了19.03%。LAVT算法应用于室内停车场环境中的车辆定位具有更好的适用性,能满足室内停车场车辆定位的基本需求。
