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鉴于弱小目标检测所固有的难点及常用的单一分辨率下的检测方法还不能准确稳定地检测出目标,提出了一种弱小目标检测新方法。考虑到实际应用中的复杂背景和大量干扰噪声,运用数据融合技术,先对图像进行小波多分辨率分解,然后将不同分辨率下的子图进行最优加权平均融合来检测弱小目标。用实地拍摄的空中弱小目标红外和可见光图像分别进行实验验证,实验图像取256×256像素点阵大小,其中目标占10×10像素左右。结果表明该方法能够准确稳定地检测弱小目标,为后续的跟踪作了很好的铺垫。 相似文献
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基于时空非局部相似性的海上红外弱小目标检测 总被引:1,自引:0,他引:1
《光子学报》2018,(11)
为了消除海上红外弱小目标检测中图像背景杂波和噪声的影响,提出了一种基于时空非局部相似性的红外图像弱小目标检测方法.该方法充分利用了相邻帧的红外图像序列间海面背景图像块的非局部自相关特性以及每帧内非局部背景图像块间的相似特性,并引入时空域图像块模型,该模型可利用加速近端梯度方法来有效求解.实验结果表明,与传统的红外弱小目标检测方法相比,所提方法不仅能更有效地保留目标的特征信息,还能使红外图像的峰值信噪比提高1.2倍以上,信杂比提高1.8倍以上. 相似文献
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鉴于弱小目标检测所固有的难点及常用的单一分辨率下的检测方法还不能准确稳定地检测出目标,提出了一种弱小目标检测新方法。考虑到实际应用中的复杂背景和大量干扰噪声,运用数据融合技术,先对图像进行小波多分辨率分解,然后将不同分辨率下的子图进行最优加权平均融合来检测弱小目标。用实地拍摄的空中弱小目标红外和可见光图像分别进行实验验证,实验图像取256×256像素点阵大小,其中目标占10×10像素左右。结果表明该方法能够准确稳定地检测弱小目标,为后续的跟踪作了很好的铺垫。 相似文献
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为了提高地面和云层等红外复杂背景下弱小目标的检测性能,提出了一种基于视觉细胞响应模型的红外弱小目标背景抑制新方法.首先利用简单细胞的感受野计算模型将原始图像采用Gabor函数卷积获得相同大小的两幅图像|然后采用设计的复杂细胞响应的非线性汇聚策略函数对获得的两幅图像进行融合处理,从而将红外图像中弱小目标和背景杂波分离,达到抑制背景的目的|最后采用自适应阈值分割技术得到目标点,实现了对红外弱小目标的检测跟踪.实验结果显示,与去局部均值和最大中值滤波两种滤波方法相比较,该方法能有效地检测出信杂比较低的弱小目标信号. 相似文献
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A hybrid moving target detection approach in multi-resolution framework for thermal infrared imagery is presented. Background subtraction and optical flow methods are widely used to detect moving targets. However, each method has some pros and cons which limits the performance. Conventional background subtraction is affected by dynamic noise and partial extraction of targets. Fast independent component analysis based background subtraction is efficient for target detection in infrared image sequences; however the noise increases for small targets. Well known motion detection method is optical flow. Still the method produces partial detection for low textured images and also computationally expensive due to gradient calculation for each pixel location. The synergistic approach of conventional background subtraction, fast independent component analysis and optical flow methods at different resolutions provide promising detection of targets with reduced time complexity. The dynamic background noise is compensated by the background update. The methodology is validated with benchmark infrared image datasets as well as experimentally generated infrared image sequences of moving targets in the field under various conditions of varying illumination, ambience temperature and the distance of the target from the sensor location. The significant value of F-measure validates the efficiency of the proposed methodology with high confidence of detection and low false alarms. 相似文献
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为了在光电成像探测目标的同时给出目标相对探测器的方位信息,探讨了一种基于单目视觉的运动目标方位测量方法。根据投射成像原理,推算出二维目标像点坐标与目标的空间三维位置之间的映射关系,建立了单目视觉测量目标方位的数学模型。结合帧间差分法和KLT方法检测静止背景下和变化背景下的运动目标,并对目标特征点进行亚像素定位。试验和仿真结果分析表明,该方法能够有效提取目标的特征点,可对目标较为准确地定位,并通过单目视觉测量出目标方位信息,误差控制在8%的范围内。 相似文献
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Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial–temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time–space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background. 相似文献
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为解决低对比度、低信噪比、目标旋转、缩放等非理想状态给跟踪算法的研究带来的诸多困难,本文提出灰度图像多特征融合目标跟踪算法,保证在满足工程实践需要的条件下,能够对目标进行稳定的跟踪。算法首先对灰度图像利用Sobel算子求出梯度特征,将X、Y双方向的梯度特征与灰度特征相融合得到新特征,新特征在核密度函数下对低对比度,目标轮廓形状变化较大的情况有较高的适应性和稳定性,再利用背景建模的方法对提取的运动目标区域进行加权,降低非跟踪目标的权值,最后对融合后的加权特征目标利用改进MeanShift算法进行跟踪。通过大量的实验表明,该算法适应目标和背景的复杂变化,并且具有较强的鲁棒性,基本满足在复杂背景灰度图像下目标跟踪的工程实际需求。 相似文献
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Wang D.Wang M. 《应用光学》2017,(1):106-113
Aiming at solving accuracy problem of infrared small target detection in sky and ocean background scenarios of infrared image sequences, a novel infrared small target detection based on multi-filters algorithm fusion method is presented in this paper. Firstly infrared small target and imaging, time and space characteristics of the corresponding background noise are analyzed. Tophat algorithm with improved Robinson guard filter are then integrated to highlight target and suppress clutter background by using infrared small target imaging features. Adaptive threshold segmentation is used to extract candidate targets, while Unger smoothing filter and multi-objects association filter are used to eliminate random noise and false targets in the candidate targets. Multiple experiments of infrared small target image sequences are implemented, and experimental results show that proposed method can detect infrared small targets at 99% detection rate with high reliability and good real-time performance. © 2017, Editorial Board, Journal of Applied Optics. All right reserved. 相似文献
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海天复杂背景下红外目标的检测跟踪算法 总被引:3,自引:2,他引:1
在分析海天复杂背景下红外目标图像特征的基础上,提出适合该环境的红外目标检测算法.该算法采用行均值相减的方法抑制海平面非线性温度场的影响,并进行中值滤波处理.对于更加复杂的环境,选用数学形态滤波法抑制背景中的大面积云团或海浪,从而确定出目标区域来进行目标图像的分割及增强.同时,综合使用图像捕获区域指定、运动目标检测法、弱目标的增强提取、记忆外推功能、数据融合加权跟踪方法,来保证在海天复杂背景下红外目标的可靠跟踪.实验表明,该算法能较好地处理海天复杂背景下红外目标的检测,且算法易于硬件实现,提高目标检测的实时效率. 相似文献
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为了提高对复杂场景下多尺度遥感目标的检测精度,提出了基于多尺度单发射击检测(SSD)的特征增强目标检测算法.首先对SSD的金字塔特征层中的浅层网络设计浅层特征增强模块,以提高浅层网络对小目标物体的特征提取能力;然后设计深层特征融合模块,替换SSD金字塔特征层中的深层网络,提高深层网络的特征提取能力;最后将提取的图像特征与不同纵横比的候选框进行匹配以执行不同尺度遥感图像目标检测与定位.在光学遥感图像数据集上的实验结果表明,该算法能够适应不同背景下的遥感目标检测,有效地提高了复杂场景下的遥感目标的检测精度.此外,在拓展实验中,文中算法对图像中的模糊目标的检测效果也优于SSD. 相似文献
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动摄像机和动目标跟踪模式下的目标检测新方法 总被引:5,自引:0,他引:5
动摄像机和动目标跟踪是图像分析中的一个难点。根据应用光学知识和坐标变换理论,提出了映射变换差分方法(mappingtransformationdifferentialmethod,MTDM)。该方法首先利用映射变换将动摄像机和动目标模式下的目标检测问题转化为技术比较成熟的静摄像机和动目标模式下的目标检测,然后利用图像差分方法检测出被跟踪目标。实验结果表明:MTDM方法在复杂天空背景下能有效地抑制背景噪声,能准确地检测出被跟踪目标。 相似文献