As the basic work of image stitching and object recognition,image registration played an important part in the image processing field.Much previous work in registration accuracy and realtime performance progressed ver...As the basic work of image stitching and object recognition,image registration played an important part in the image processing field.Much previous work in registration accuracy and realtime performance progressed very slowly,especially in registrating images with line feature.An innovative method for image registration based on lines is proposed,it can effectively improve the accuracy and real-time performance of image registration.The line feature can deal with some registration problems where point feature does not work.Our registration process is divided into two parts.The first part determines the rough registration transformation relation between reference image and test image.Then the similarity degree among different transformation and modified nonmaximum suppression(MNMS)algorithms are obtained,which produce local optimal solution to optimize the rough registration transformation.The final optimal registration relation can be obtained from two registration parts according to the match scores.The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.展开更多
针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所...针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所在的范围,通过基于RSSI的机器人自定位系统进行机器人位置的进一步精确定位。求解过程中,通过遗传算法求解极大似然方程组,并提出染色体的筛选和剔除策略。仿真实验结果表明,该方法在有效的时间内完成定位,平均定位误差为0.157 2 m,与传统的改进方法 0.332 14 m的定位误差相比,降低了近一倍。并且新方法受环境影响较小,鲁棒性较好,能够很好地满足室内移动机器人的定位要求。展开更多
随着无线传感器网络的发展,日益需要更加精确的位置信息来支撑其相关的应用。通过分析待定位节点定位过程中产生的误差,对二阶段定位算法、接收信号强度指示(received signal strength indicator,RSSI)定位技术和质心算法进行深入的研究...随着无线传感器网络的发展,日益需要更加精确的位置信息来支撑其相关的应用。通过分析待定位节点定位过程中产生的误差,对二阶段定位算法、接收信号强度指示(received signal strength indicator,RSSI)定位技术和质心算法进行深入的研究,提出了极大似然与加权质心混合定位算法:首先通过极大似然估计法对待定位节点进行粗略估计,然后利用加权质心算法对待定位节点坐标估计求精,进一步提高定位精度。仿真实验结果表明,该算法能够在定位精度方面有较大的提高。展开更多
提出了一种求解最大团问题的自适应过滤局部搜索算法AF-RLS(adaptive filtered-reactive local search).该算法通过构建独立集约束,优选出有希望的邻域移动方向来提高局部搜索趋向最优解的概率;并在比较分析两种不同逃逸策略的逃逸能力...提出了一种求解最大团问题的自适应过滤局部搜索算法AF-RLS(adaptive filtered-reactive local search).该算法通过构建独立集约束,优选出有希望的邻域移动方向来提高局部搜索趋向最优解的概率;并在比较分析两种不同逃逸策略的逃逸能力和逃逸代价的基础上,提出了基于问题解空间结构自适应设置局部搜索深度参数的方法.基于漂移分析理论和在37个典型测试算例上的实验结果表明,所提出的AF-RLS算法相比原RLS算法性能有明显改善.展开更多
文摘As the basic work of image stitching and object recognition,image registration played an important part in the image processing field.Much previous work in registration accuracy and realtime performance progressed very slowly,especially in registrating images with line feature.An innovative method for image registration based on lines is proposed,it can effectively improve the accuracy and real-time performance of image registration.The line feature can deal with some registration problems where point feature does not work.Our registration process is divided into two parts.The first part determines the rough registration transformation relation between reference image and test image.Then the similarity degree among different transformation and modified nonmaximum suppression(MNMS)algorithms are obtained,which produce local optimal solution to optimize the rough registration transformation.The final optimal registration relation can be obtained from two registration parts according to the match scores.The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.
文摘针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所在的范围,通过基于RSSI的机器人自定位系统进行机器人位置的进一步精确定位。求解过程中,通过遗传算法求解极大似然方程组,并提出染色体的筛选和剔除策略。仿真实验结果表明,该方法在有效的时间内完成定位,平均定位误差为0.157 2 m,与传统的改进方法 0.332 14 m的定位误差相比,降低了近一倍。并且新方法受环境影响较小,鲁棒性较好,能够很好地满足室内移动机器人的定位要求。
文摘随着无线传感器网络的发展,日益需要更加精确的位置信息来支撑其相关的应用。通过分析待定位节点定位过程中产生的误差,对二阶段定位算法、接收信号强度指示(received signal strength indicator,RSSI)定位技术和质心算法进行深入的研究,提出了极大似然与加权质心混合定位算法:首先通过极大似然估计法对待定位节点进行粗略估计,然后利用加权质心算法对待定位节点坐标估计求精,进一步提高定位精度。仿真实验结果表明,该算法能够在定位精度方面有较大的提高。
文摘提出了一种求解最大团问题的自适应过滤局部搜索算法AF-RLS(adaptive filtered-reactive local search).该算法通过构建独立集约束,优选出有希望的邻域移动方向来提高局部搜索趋向最优解的概率;并在比较分析两种不同逃逸策略的逃逸能力和逃逸代价的基础上,提出了基于问题解空间结构自适应设置局部搜索深度参数的方法.基于漂移分析理论和在37个典型测试算例上的实验结果表明,所提出的AF-RLS算法相比原RLS算法性能有明显改善.
