Detection and tracking of targets in infrared images using Bayesian techniques

Simple yet robust techniques for detecting targets in infrared (IR) images are an important component of automatic target recognition (ATR) systems. In our previous works, we have developed IR target detection and tracking algorithms based on image correlation and intensity. In this paper, we discuss these algorithms, their performances and problems associated with them and then propose novel algorithms to alleviate these problems. Our proposed target detection and tracking algorithms are based on frequency domain correlation and Bayesian probabilistic techniques, respectively. The proposed algorithms are found to be suitable for real-time detection and tracking of static or moving targets, while accommodating for detrimental affects posed by the clutter and background noise. Finally, limitations of all these algorithms are discussed.     

Automatic detection of small objects from their infrared state-of-polarization vectors

A technique for automatic detection of targets from their infrared signature's state-of-polarization vector is described. The bounds on the Bayesian total probability of errors are estimated from the observed Stokes vector imagery and used as metrics for separating targets from background clutter. The performance of the proposed approach for objects under various geometries is studied in terms of receiver operating characteristic curves. The new results, which have been obtained from data from the U.S. Air Force's Infrared Modeling and Analysis polarimetric infrared simulation tool, indicate the usefulness of polarimetric infrared signatures for the automatic detection of small targets.     

New polarimetric signal subspace detectors for SAR processors

Our studies aim at detecting targets embedded in a complex environment for radar applications. This article deals with new polarimetric SAR (synthetic aperture radar) processors based on subspace detectors. These algorithms use models of targets including physical and polarimetric properties of their scattering on the contrary to the isotropic point model that is commonly used. These processors are implemented by computing the corresponding subspaces that contain the relevant responses of the target. We choose for application the detection of targets under forest foliage. The obtained results both on simulated data of realistic targets and real data show the interest of these new processors.     

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.     

海天复杂背景下红外目标的检测跟踪算法

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.     

Infrared dim target detection technology based on background estimate

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, two new algorithms – background estimate and frame difference fusion method, and building background with neighborhood mean method are presented. The basic principles and the implementing procedure of these algorithms for target detection are described. Using these algorithms, the experiments on some real-life IR images are performed. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective view and objective view. 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.     

基于目标与环境FD模型的多特征检测算法适应性评估

智能变形、变色、变温、变谱技术发展趋势下,低特征目标加速实现与自然地物背景的特征融合,导致复杂自然背景环境下低散射、微反射、弱辐射目标的检测与评估愈发困难,特定场景下潜在威胁目标的检测方法快速决策与准确评估成为了难题.为了提升离散目标、伪装目标、弱小目标、异常目标等低特征目标与复杂自然背景环境融合场景下的多特征检测算法...     

偏振光谱图像去雾技术研究

空气污染严重影响了成像质量,雾霾天气下成像的对比度和分辨率下降。利用不同波段偏振光谱数据的图像去雾研究国内外尚未见报道;运用偏振光谱去雾技术对光谱数据立方体研究具有重要意义。在中重度雾霾天气下,利用光谱仪,获取了380～1 000 nm的可见近红外偏振光谱数据,研究了450,550,650,750和850 nm五个波长下偏振图像和去雾结果。结果表明,波长对图像目标与背景的灰度差值影响显著,去雾后图像的灰度动态范围和平滑度都有显著提升;去雾后图像远景对比度平均提升了5倍,其中450 nm图像对比度最低,但对比度提升量最大,提高了7.13倍;850 nm图像对比度最高,但对比度提升量最小,提高了3.86倍;去雾后不同波长图像的灰度动态范围均有明显展宽,展宽范围为13.5%～28.6%,而图像近景动态范围展宽为33.3%～44.0%。分析了图像复原过程中可能出现的估计误差,根据其产生机理提出了对大气散射光的偏振度和无穷远处的大气散射光强度的校正方法,并总结出了最佳校正系数的选取规律,对未知条件下的图像去雾提出指导。通过斯托克斯参数从原始数据中提取偏振信息,基于大气散射光与景物透射光偏振特性差异对图像去雾,偏振光谱图像去雾技术为光谱图像去雾研究提供一种新手段,也为图像去雾技术的发展提供一种新思路。     

基于形状特征的水声图像小目标识别方法

为了抑制背景噪声,提高目标识别准确率,该文提出一种基于形状特征的水声图像小目标识别方法。对含有目标的水声图像进行非局部均值去噪处理后,使用OTSU算法自适应选取阈值对去噪图像进行二值化分割,结合形态学处理获得分割后的目标区域;提取目标区域的矩形度、圆形度、几何不变矩等各项形状参数,将目标的特征向量输入随机森林分类器实现对目标形状的识别。在仿真和实测数据集上分别进行了实验,结果表明,该方法对水声图像中的目标具有较高的识别率,可以实现不同高斯噪声背景下的目标识别,相较于其他方法在识别率上有一定提高。     

基于FPGA的运动目标实时检测跟踪算法及其实现技术

运动目标检测跟踪有关的算法及其基于PC平台的实现已经比较成熟,但实时性较差。将采集的彩色视频流分成灰度和彩色两个数据流,灰度视频用于目标检测,彩色视频流用于跟踪显示。以经典的帧间差分法和背景差分法为基础,根据现场可编程门阵列(FPGA)的特点及片外同步动态存储器的存取控制要求,对这两个算法用FPGA逻辑单元进行了设计和实现。对原始彩色视频流和转换后的灰度视频流的存取使用乒乓操作,在滤波和形态学处理时使用了并行的流水线操作,极大地提高了算法的实时处理能力。在FPGA开发板上构建了一个彩色视频图像中运动目标检测跟踪系统,对系统性能进行了测试。实验结果表明,系统可在多种分辨率和帧率下进行运动目标进行实时检测跟踪;固定背景差分法对目标运动速度无限制,但当使用帧差法对快速运动目标进行有效的检测时,应使目标的帧差间距大于3.2像素。     

自然地面背景下红外目标检测算法

为了快速准确地检测出以地面物体为背景的红外序列图像中的目标,结合小波变换的特点及其应用于图像边缘检测的优点,提出了一种基于小波技术的图像预处理算法。该方法利用图像边缘在目标识别中的重要作用,讨论了基于小波变换的图像边缘检测方法,并结合地面背景红外图像自身的特点,大大地提高了单帧的检测概率。     

Aircraft recognition based on improved iterative threshold selection and skeleton Zernike moment

Using the visible optics images to identify targets is an important part in the development of remote sensing technology. In this paper, a new aircraft recognition method based on the improved iterative threshold selection and the skeleton Zernike moment is presented. The method segment aircraft targets under complex background using iterative threshold selection with between-class distance and scatter, and calculate the skeleton Zernike moment for the aircraft target recognition using template matching method. The experimental results show that the new method can effectively achieve the target segmentation under complex backgrounds, and provide a satisfactory performance both in recognition rate and recognition speed.     

基于背景粗糙度估计的红外目标检测算法

在分析海天背景下目标红外图像特征的基础上,提出了一种基于背景粗糙度估计的红外目标自适应检测算法.该算法利用LOG算子进行目标基本轮廓检测,将目标的中心点作为区域生长的种子点,通过背景粗糙度估计自适应确定LOG算子参量,完成目标图像分割.与传统检测方法比较,采用该处理方法进行小目标检测及识别过程的预处理,可以有效地减少运算量,提高检测速度,抑制对不必要的种子点进行区域生长,提高了目标的检测概率.     

4×2×3阵列式放大器参数测量系统优化设计

采用软边光阑和多级空间滤波器组合技术实现了光束的空间整形和等像面传输，利用Fresnel光束传输计算程序对4×2×3阵列式放大器参数测量系统进行了优化设计，分析了空间滤波器中小孔尺寸对光束波面传输的影响，并对优化设计结果进行了分析和讨论，所用方法和所得结论对高功率固体激光放大系统的设计有应用意义。     

被动式人体太赫兹图像中的目标辅助识别

提出一种被动式人体太赫兹安检仪的图像目标标注方法,用于辅助识别被安检人员隐藏携带的违禁物品。分析了探测器中心频率为0.1THz的被动式太赫兹人体安检仪图像特点,根据其特点进行图像增强、分割、轮廓提取等处理,进而实现图像中的目标检测,将其目标位置映射在同场景同时间采集的可见光图像上。实验结果表明,该方法可以有效地提取并标示可疑目标,帮助快速发现衣物下的隐匿物品,提高太赫兹安检仪的实用性。     

基于光谱探测与图像识别的反伪装目标系统研究

为了在野外环境中快速有效地识别敌方伪装的机动目标,设计了基于光谱探测与视频图像目标识别方法联用的目标识别系统。采用视频图像识别技术获取被测区域的二维影像,再通过光谱探测技术识别目标,最终将目标重建在图像相应位置上从而实现目标识别的可视化。理论推导得到了系统可识别目标的函数关系式,根据该函数关系进行了目标识别的量化实验。实验采用汽车模拟被测机动目标,在不同距离上分别以平坦荒地、灌木丛和废弃建筑物为背景,对明显目标、涂覆迷彩色的目标以及遮挡伪装物的目标分别进行光谱探测。实验结果显示,测试背景对光谱探测效果有一定影响,背景的连续性有利于目标识别;伪装方式以伪装物遮挡最难识别,且随着目标与系统的距离增大而信噪比随之降低。综上所述,采用光谱探测技术克服了传统图像目标识别无法识别伪装目标的缺点,可以实现对伪装目标的有效识别。     

基于最小平均距离免疫算法的模糊红外图像分割

由于犯罪分子利用各种方法来避开传统的刑侦图像技术,因而红外图像逐渐成为获取犯罪现场痕迹的有效手段。然而,从犯罪现场拍摄的红外图像其目标痕迹大多是弱化的,所以在这类红外图像中分割目标是一项具有挑战性的任务。已有基于生物免疫的各类算法尚未明确描述免疫分割作用领域,以及免疫网络算法模型中的免疫识别距离。为实现对目标痕迹弱化红外图像的有效分割,提出了一种新的具有免疫作用领域和最小平均免疫识别距离的人工免疫构架,设计了一种具备最小平均距离免疫域的免疫分割算法。该方法根据红外图像的特点,采用多步分类算法、免疫变异和自适应免疫最小均距识别方法,根据目标区域和背景区域的总体统计特性实现最佳分类。实验结果表明,提出的基于最小平均距离的免疫算法能够有效地分割目标弱化的红外图像。与经典的边缘模板和区域模板方法相比,该算法具有更好的分割效果,尤其是针对目标弱化红外图像的分割,该算法能够较好地给出五个手指的边界轮廓。     

红外小目标图像的背景杂波量化方法

分析了背景杂波对红外小目标检测的影响,研究了一种针对红外小目标图像的背景杂波定量描述方法.提出了背景杂波定量描述方法需要满足的三个条件:与主观判断的结果一致、对于不同的红外图像具有适应性和能够辅助改进目标检测算法.综合考虑了目标与背景的特征,融合了4种特征,提出了一种新的红外背景杂波量化方法.首先采用支持向量机对背景杂...     

一种基于特征提取的相关跟踪算法

介绍了一种能稳定快速跟踪复杂背景下目标的算法,该算法在传统相关跟踪算法的基础上进行改进.当目标进入红外(电视)摄像机视场时,视频信号中包含有目标信息和背景信息,信号处理器先将此信号进行数字化处理,形成具有一定灰度等级的数字化图像阵列,然后采用边缘检测、阈值分割等算法对包含有目标信息的图像进行边缘处理,提取出具有特征的目...     

红外图像掠海小目标的检测算法研究

针对红外图像掠海小目标信噪比低，且易受到水天线和背景杂波干扰的特点，提出了一种红外掠海小目标的提取与检测算法。该方法的特点是：首先采用中值滤波来减小噪声，并提出了差方和算法，用以抑制背景噪声并对目标增强；再采用了图像行扫描法有效地滤除水天线；最后通过弱化背景边缘和阈值分割等综合算法得到候选小目标。仿真结果表明，该算法达到了较好的效果。