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1.
模板匹配的分类方法是合成孔径雷达目标识别的重要方法。研究了MSTAR SAR图像,指出图像中的目标峰值点本质上对应于实际物体的散射中心,并且在一定方位区间中具有稳定性。假设目标存在位置偏移的基础上,提出先利用目标峰值点对准目标,再生成模板和分类的思想。分析了方位区间中基准图像的选取及对准过程对模板生成和目标分类的影响程度。最后研究了同类变形目标间的分类,提出了峰值点特征增强成像、预分类、目标对准和正式分类的四步算法。结果证明,所提算法具有较高的分类精度和效率。  相似文献   
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Image mosaicking is widely used in Geographic Information Systems (GISs) for large-scale ground surface analysis. However, most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation. In this paper, we propose a novel and practical algorithm for real-time infrared video mosaicking. To achieve this, a novel fast template matching algorithm based on Sum of Cosine Differences (SCD) is proposed to coarsely match the sequential images. The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform (FFT). We also propose a novel fast Least Squares Matching (LSM) algorithm for inter-frame fine registration, which can significantly reduce the computation without degrading the matching accuracy. In addition, the proposed fast LSM can effectively adapt for noise degradation and geometric distortion. Based on the proposed fast template matching algorithm and fine registration algorithm, we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.  相似文献   
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蔡佳  黄攀峰  王东科  张彬 《宇航学报》2015,36(2):133-141
为解决空间绳系机器人近距离交会对接中的目标测量问题,设计了一种单目视觉伺服控制系统。该系统针对小模板在大场景中匹配时存在鲁棒性差、耗时长等缺点,采用了一种新的模板匹配算法。匹配模板借鉴FAST算法的空心圆环结构,使得算法具有旋转不变性,也减小了因背景变化引起的累积误差。同时匹配准则采用灰度值+梯度值的复合算子,具有较强的抗噪声和抑制光照变化的能力。为避免因错误匹配和遮挡等引起的跟踪失败,还设计了动态模板更新策略;最后结合最小二乘综合位置预测方法和PD控制器,实现了对复杂场景中的非合作目标的实时跟踪逼近。  相似文献   
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复杂背景下目标模板更新算法研究   总被引:4,自引:0,他引:4  
在匹配跟踪算法中,模板更新策略是跟踪成败的关键。从复杂背景下的目标跟踪出发,指出固定模板和逐帧模板更新的不足之处,介绍几种性能较好的模板更新策略,并在此基础上提出改进的模板更新方法。同时,为了更好地克服背景光照带来的影响,对普通最小平均绝对差值函数MAD(M in imum Absolute D ifference)方法进行了改进。仿真实验结果表明,该算法能克服诸如烟尘干扰、背景光照变化、地貌起伏等不利因素,能在复杂背景中稳定和精确地进行目标跟踪。  相似文献   
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基于相关匹配的弹道中段目标章动角估计   总被引:1,自引:0,他引:1  
肖立  周剑雄  卢再奇  陈光辉 《宇航学报》2010,31(12):2635-2643
章动角是弹道中段目标的重要进动特征,可用于识别弹头和诱饵。首先提出了基于相关匹配的章动角估计方法,将观测的目标雷达截面积(RCS)序列与不同参数下的RCS序列模板进行相关匹配,搜索匹配函数的最大值,从而得到章动角的估计值。然后给出了具体的实现算法,将初始时刻的采样间隔设置为脉冲重复周期(PRI)的整数倍或1/K(K为整数,且K≥2),在此基础上减少了匹配函数的计算量,并且可通过单层或多层相关匹配来估计章动角。仿真结果表明,由于RCS测量存在系统误差,相关匹配的章动角估计性能远优于最小二乘匹配。同时分析了全姿态RCS数据的角度采样间隔对相关匹配估计性能的影响。最后比较了多层相关匹配和单层相关匹配的估计性能和计算量。  相似文献   
9.
《中国航空学报》2021,34(12):214-226
In the aviation industry, cable bracket is one of the most common parts. The traditional assembly state inspection method of cable bracket is to manually compare by viewing 3D models. The purpose of this paper is to address the problem of inefficiency of traditional inspection method. In order to solve the problem that machine learning algorithm requires large dataset and manually labeling of dataset is a laborious and time-consuming task, a simulation platform is developed to automatically generate synthetic realistic brackets images with pixel-level annotations based on 3D digital mock-up. In order to obtain accurate shapes of brackets from 2D image, a brackets recognizer based on Mask R-CNN is trained. In addition, a semi-automatic cable bracket inspection method is proposed. With this method, the inspector can easily obtain the inspection result only by taking a picture with a portable device, such as augmented reality (AR) glasses. The inspection task will be automatically executed via bracket recognition and matching. The experimental result shows that the proposed method for automatically labeling dataset is valid and the proposed cable bracket inspection method can effectively inspect cable bracket in the aircraft. Finally, a prototype system based on client-server framework has been developed for validation purpose.  相似文献   
10.
为实现复杂涡轮叶片外形模具分模自动化,提出了一种基于特征分解及模板库的涡轮叶片分模方法.该方法运用离散和集成的思想,采用自动特征识别和人机交互特征识别的方式提取型腔体模型分解特征,将涡轮叶片的核心包络块分解为简单特征体,然后针对每个简单特征体,采用基于模板库的分模方法实现自动分模,从而有效地提高了涡轮叶片外形模具的设计...  相似文献   
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