基于运动方向估计的管道滤波算法

管道滤波算法提出了从时域角度解决弱小目标检测问题的思路,对于红外强起伏天空背景中弱点目标的检测问题,管道内强噪音的干扰以及低信噪比的条件会导致检测概率降低的情况出现.本文提出了一种运动方向估计的管道滤波算法,分析了红外弱点目标的运动特性,依据弱点目标在相邻帧间位置具有连贯性的特征,建立了弱点目标的运动方向估计模型.在模型中利用弱点目标逐帧检测的先验位置信息,估计弱点目标的运动方向和轨迹,根据估计结果去除管道内噪音对弱点目标的干扰.仿真结果表明,该方法能够很好地抑制管道内噪音的影响,提高弱点目标的检测概率,增强弱点目标抗管道内噪音干扰的能力.     

水下目标多平台协同定位和跟踪方法

提出了一种融合多平台方位观测数据的水下声学目标定位跟踪方法。该方法采用多级多假设跟踪对目标状态进行滤波和关联,第一级基于每个平台波束域检测结果独立使用多假设跟踪方法对目标方位角进行初级跟踪;第二级首先通过多平台联合似然概率估计多目标初始位置,再用多假设跟踪方法对不同平台初级跟踪的方位角航迹进行关联,最后通过关联的角度值和扩展卡尔曼滤波对多目标位置进行序贯估计。仿真结果证明,该方法能以更少计算量实现更优定位性能,优于单级多假设跟踪方法.海试数据处理结果表明,存在较多干扰的情况下,单级方法跟踪过程中丢失目标,而多级方法实现了对目标的全程跟踪,且收敛后定位误差在70m内。该方法可降低噪声干扰对数据关联及目标定位的影响,并通过多平台协同消除线阵的方位模糊,适用于移动多平台声学目标定位和跟踪场景。     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

基于视觉SLAM和目标检测的语义地图构建

语义信息对于移动机器人理解环境内容、执行复杂任务至关重要,针对ORB-SLAM2构建的点云过于稀疏、缺乏语义信息、点云所占存储空间大等问题,提出将目标检测算法与视觉SLAM(同时定位与地图构建)技术紧密结合,构建环境的稠密点云语义地图.首先,通过目标检测网络YOLO v3及对象正则化准确获取物体的2D标签,并经过ORB...     

基于多结构元素形态滤波与自适应阈值分割相结合的红外弱小目标检测

针对低信噪比灰度图像中弱小目标检测的难题,分析了红外弱小目标成像的特点,提出了基于多结构元素形态滤波与自适应阈值分割相结合的目标检测算法.利用目标运动的连续性、规律性和噪音产生的随机性,结合数学形态学结构元素的特点,研究了一种多结构元素形态滤波的管道滤波方法,通过流水线管道检测目标运动轨迹.实验结果表明,该算法应用于复杂背景下低信噪比的红外弱小目标图像能够得到较理想的结果,并且目标检测概率高,速度快,虚警率低.     

Multispectral registration method based on stellar trajectory fitting

Image registration techniques for detecting moving targets in space require high temporal resolution and multispectral images to improve the target detection probability and reduce the false positive rate. At present, image registration accuracy is affected by the effective number of common multispectral registration control points as well as its stability. Image registration based on on-orbit stellar trajectory measurement can be used to perform on-orbit modifications of registration deviation caused by thermal distortions during launch. This study proposes a new image registration method based on the on-orbit detection of stars by using the stellar trajectory on a camera’s focal plane. A generated 12?×?12 data template and the Lagrange interpolation method are used in the registration model. Multispectral image registration based on stellar trajectory fitting can achieve high-precision image registration among different spectra.     

Infrared small target detection based on directional zero-crossing measure

Infrared small target detection under complex background and low signal-to-clutter ratio (SCR) condition is of great significance to the development on precision guidance and infrared surveillance. In order to detect targets precisely and extract targets from intricate clutters effectively, a detection method based on zero-crossing saliency (ZCS) map is proposed. The original map is first decomposed into different first-order directional derivative (FODD) maps by using FODD filters. Then the ZCS map is obtained by fusing all directional zero-crossing points. At last, an adaptive threshold is adopted to segment targets from the ZCS map. Experimental results on a series of images show that our method is effective and robust for detection under complex backgrounds. Moreover, compared with other five state-of-the-art methods, our method achieves better performance in terms of detection rate, SCR gain and background suppression factor.     

基于核各向异性扩散的红外小目标检测

为了减少红外图像中背景边缘对检测的影响,提出了一种具有鲁棒性的弱小目标检测算法,该算法利用核各向异性扩散模型进行背景预测,再与原图像差分实现弱小目标检测。为了提高算法的自适应能力,提出了一种鲁棒性扩散系数,能够根据图像背景的起伏程度自适应调整扩散系数曲线的陡峭程度。实验结果表明,与现有的检测算法相比,该算法能够在不同类型的复杂背景下有效抑制背景及其边缘,保留目标大小,降低虚警率,具有更强的鲁棒性。     

基于核各向异性扩散的红外小目标检测

为了减少红外图像中背景边缘对检测的影响,提出了一种具有鲁棒性的弱小目标检测算法,该算法利用核各向异性扩散模型进行背景预测,再与原图像差分实现弱小目标检测。为了提高算法的自适应能力,提出了一种鲁棒性扩散系数,能够根据图像背景的起伏程度自适应调整扩散系数曲线的陡峭程度。实验结果表明,与现有的检测算法相比,该算法能够在不同类型的复杂背景下有效抑制背景及其边缘,保留目标大小,降低虚警率,具有更强的鲁棒性。     

一种背景自适应调整的弱点目标探测算法

针对因复杂背景导致低信噪比的弱点目标探测率降低的问题,首先分析了从红外图像中探测弱点目标时,由于复杂和缓变背景下潜在目标探测率不同,而导致目标探测率降低的理论依据;并在该分析的基础上,提出了一种基于背景自适应调整的红外点目标探测算法。该方法利用鲁宾逊(Robinson)保护滤波器从经过预处理的图像中提取潜在目标;通过复杂背景模糊隶属度函数将图像映射到模糊特征平面,并由该特征平面计算背景调整因子,以对提取的潜在目标进行加权调整,从而降低了复杂背景的影响。实验结果表明,该算法可以显著提高复杂背景下红外点目标的检测概率,并且能够探测出信噪比为1的目标。     

Prediction of the performance of an algorithm for the detection of small targets in infrared images

Detection of small targets in infrared (IR) images is important in IR image processing. For the prediction of performance of a detection algorithm, it is necessary to calculate the probability of detection and probability of false alarm. A method is developed to calculate the probabilities in this paper. The detection is divided into two parts: the first part, which is called pre-detection, is to find out candidates for targets in a single frame of an image; and the second part is to localize the target in multiple frames of the image. Under some assumptions, the pre-detection probability, the false detection probability of single frame, detection probability and false alarm probability are derived. The algorithm for the detection of small target in IR image, which is used for the derivation of the probabilities, is contrast threshold detection based on background prediction, and a pipeline filter is used for multiframe image processing. The results show the relationship of the probabilities to the contrast of target to background, SNR, and contrast threshold.     

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.     

Synchronization of mobile chaotic agents on connected networks

This work studies the synchronization of a number of mobile agents on a substrate network. Each agent carries a chaotic map and randomly walks on a connected network. The collection of agents consists of another time-varying network derived from the substrate network. It is found that the synchronization conditions of this agent network depend on the average degree of the substrate network’s connectivity, the coupling strength between interacting agents, and the agent density in the network. Synchronization of the agent network on scale-free and ER networks is considered here, and it is found that the scale-free topology is more applicable to synchronize mobile chaotic agents. To get analytical insights, the star graph is taken and considered as a substrate network.     

强杂波背景红外弱小目标检测算法

针对强杂波背景远距离红外弱小信号目标的特点,提出了一种基于自适应滤波的红外弱小信号检测方法。算法首先对图像进行消噪声处理,其次运用自适应滤波方式消除背景增强目标信号,最后进行基于点源目标（试验采集）成像信号特性的判决法则删除虚假目标,算法有效解决了光电探测设备高检测概率与低虚警率的矛盾。实验结果表明：该方法能够在单帧图像上有效提取出小区域信噪比为4的弱小信号目标,检测概率不低于0.75,虚警率不高于1次/100帧。     

强杂波背景红外弱小目标检测算法

针对强杂波背景远距离红外弱小信号目标的特点,提出了一种基于自适应滤波的红外弱小信号检测方法。算法首先对图像进行消噪声处理,其次运用自适应滤波方式消除背景增强目标信号,最后进行基于点源目标(试验采集)成像信号特性的判决法则删除虚假目标,算法有效解决了光电探测设备高检测概率与低虚警率的矛盾。实验结果表明:该方法能够在单帧图像上有效提取出小区域信噪比为4的弱小信号目标,检测概率不低于0.75,虚警率不高于1次/100帧。     

Renormalization group analysis of the global properties of a strange attractor

This paper considers the circle map at the special point: the one at which there is a trajectory with a golden mean winding number and at which the map just fails to be invertable at one point on the circle. The invariant density of this trajectory has fractal properties. Previous work has suggested that the global behavior of this fractal can be effectively analyzed using a kind of partition function formalism to generate anf versus curve. In this paper the partition function is obtained by using a renormalization group approach.     

联合多站阵元域数据的水下目标检测与跟踪

为了提高复杂海洋环境中目标的检测、跟踪性能,提出一种联合多站阵元域数据的水下目标检测与跟踪方法.该方法采用序列马尔科夫链蒙特卡洛思想对目标进行采样更新,通过对接收概率中的后验概率以及采样函数进行分解展开,并根据多站阵元域数据计算采样粒子的联合似然,在迭代过程中实现目标数目和目标状态的联合估计.研究结果表明,该方法对单目标的平均定位误差在较高信噪比下能够稳定在50 m以内,对多目标随机出入场景中新生及消失目标实现有效检测,同时对强干扰下弱目标及交叉目标实现有效检测跟踪。仿真结果和海试数据均验证该方法具有良好的目标检测与跟踪性能。     

Infrared dim target detection based on visual attention

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. Based on human visual attention mechanisms, an automatic detection algorithm for infrared dim target is presented. After analyzing the characteristics of infrared dim target images, the method firstly designs Difference of Gaussians (DoG) filters to compute the saliency map. Then the salient regions where the potential targets exist in are extracted by searching through the saliency map with a control mechanism of winner-take-all (WTA) competition and inhibition-of-return (IOR). At last, these regions are identified by the characteristics of the dim IR targets, so the true targets are detected, and the spurious objects are rejected. The experiments are performed for some real-life IR images, and the results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.     

高灵敏度空间点目标探测系统设计

本文面向空间点目标探测,设计了基于高灵敏度CMOS传感器的空间点目标探测系统。首先对CMOS传感器图像进行降噪,提高传感器的探测灵敏度;其次,采用DSP+FPGA嵌入式架构,设计了基于星图匹配信息构建的点目标探测算法,并详细介绍了算法原理和步骤。最后,采用电子星图模拟器对该探测系统进行测试。结果表明:该嵌入式系统具备1 024×1 024@20p格式视频的实时处理能力,可以探测6等星。当信噪比大于6,视轴指向误差小于1°时,对于不同运动速度、不同尺寸点目标均能准确探测,识别正确率接近100%。综合而言,该空间点目标探测方法的计算精度高、适应性强、可靠性高,能够应用于空间点目标的有效探测。     

Metabolism-enhanced tumor localization by fluorescence imaging: in vivo animal studies

We present a high-sensitivity near-infrared optical imaging system for noninvasive cancer detection and localization based on molecularly labeled fluorescent contrast agents. This frequency-domain system utilizes the interferencelike pattern of diffuse photon density waves to achieve high detection sensitivity and localization accuracy for the fluorescent heterogeneity embedded inside the scattering media. A two-dimensional localization map is obtained through reflectance probe geometry and goniometric reconstruction. In vivo measurements with a tumor-bearing mouse model by use of the novel Cypate-mono-2-deoxy-glucose fluorescent contrast agent, which targets the enhanced tumor glycolysis, demonstrate the feasibility of detection of a 2-cm-deep subsurface tumor in the tissuelike medium, with a localization accuracy within 2-3 mm.