低对比度红外图像点目标运动检测方法

对于背景对比度较低的红外图像,若点目标的亮度不明显,加上探测器噪声的影响,很难分辨出红外图像中处于运动状态的点目标.为了解决这一问题,对探测器采集的相邻2幅图像进行基于灰度峰值与傅里叶相位相关算法的图像配准,计算出2幅图像之间的相对平移量;通过对2幅图像的重叠区域进行差分绝对值、高斯低通滤波及二值化运算;分离出运动的点目标.实验结果表明:检测方法可以有效地检测出低对比度红外图像中的运动点目标.     

运动背景下的运动目标检测方法

对运动目标检测问题,提出了一种运动背景下的运动目标检测新方法,为了配准图像首先利用傅立叶变换的相位相关法进行背景补偿,然后采用六帧相邻图像差分相乘的方法增强运动目标的像素点,拉大目标与背景残留噪声的差异,利用区域-分裂合并法进行目标分割,将运动目标检测出来;从而计算出运动目标的形心坐标,并绘出运动目标的运动轨迹.由于方...     

基于互相关信息的图像目标检测方法研究

针对背景变化下运动目标的检测问题,采用一种结合互相关信息的目标检测算法,利用混合优化,Powll和粒子群(Pso)交替优化两幅图像间的互相关信息,直到互信息值最大,得到图像间的最佳平移旋转参数,将图像进行空间配准.最后采用三帧差分相乘法检测出目标并增强目标区域,实现运动目标的准确检测.通过对变化背景图像的运动目标检测仿真,结果证明了检测算法的准确性和可行性.     

结合相位一致与全变差模型的高分辨率遥感图像边缘检测

目的边缘检测是有效利用遥感数据开展地物目标自动识别的重要步骤。高分辨率遥感图像地物类型复杂,细节信息过于丰富,使得基于相位一致的边缘检测结果中存在过多的噪声与伪边缘。为此提出了一种结合相位一致与全变差模型的高分辨率遥感图像边缘检测方法。方法根据相位一致原理,应用Log Gabor构造的2维相位一致模型,引入全变差去噪模型对基于相位一致的边缘强度图进行改进。结果借助有界变差空间对图像光滑性的约束,实现了高分辨率遥感图像噪声去除与伪边缘抑制,利用改进后的相位一致边缘强度图可有效检测高分辨率遥感图像的边缘。结论实验结果表明,与相位一致模型、Canny算法相比,该方法能消除了高分辨率遥感图像中同类地物内部细节特征形成的噪声,抑制相位一致边缘检测结果中的伪边缘,突出地物的真实边缘,并能正确地提取地物目标的整体轮廓信息,有助于后续地物目标的自动识别。     

图像综合特征及其在图像检测与匹配中的应用

针对灰度,几何畸变较大的图像匹配困难的问题,提出了一种图像匹配的新方法。该方法在图像预处理时,首先利用SUSAN算法来检测图像目标的边缘,然后利用图像不变矩,并结合形态信息、灰度信息构造的图像综合特征来进行图像匹配,以完成目标的识别与跟踪。由于SUSAN算法检测特征定位准确,对局部噪声不敏感,而且不变矩具有平移、旋转、比例不变的特性,因此可取得较好的检测与匹配效果。实验也表明,该算法既具有较强的抗灰度、抗几何畸变能力,又具有较强的噪声抑制能力。     

动态背景下的运动目标检测

针对动态背景下运动目标检测的问题,最大限度地降低背景对运动目标检测的影响,提出了一种基于相位相关法和傅里叶梅林变换的动态背景下运动目标检测算法.动态背景下运动目标检测的主要部分是背景运动补偿,首先利用相位相关法和傅里叶梅林变换估计全局运动参量,然后根据全局运动参量利用双线性内插法进行背景匹配,最后对配准后的图像利用帧间差分法提取运动目标.实验表明,该算法具有一定的鲁棒性,能有效地检测动态背景下的运动目标.     

一种视频图像目标平移运动估计算法

运动估计是数字视频处理的基本问题之一,目前广泛采用的是相位关法,由于频谱泄漏和边界效应等原因,其运动估计的准确性难以保证。为此,在讨论基本的相位相关算法的基础上,引入一种用于构造二维圆对称窗函数的核回归方法,提出基于加窗预处理的相位相关法,并将其应用于运动目标的平移运动估计。对比实验结果表明,该方法能够有效地降低频谱泄漏和边界效应对运动估计的影响,提高了相位相关法对运动目标平移估计的正确性。     

基于双树复小波变换的相位保持SAR图像降噪

为更有效地抑制噪声,提出了一种基于非正交复值log-Gabor小波变换的SAR图像斑点噪声消除算法。该算法通过相位保持消噪的门限操作确保相位信息不受破坏。由于用单一的乘性模型或加性模型消除SAR图像的斑点噪声都不能取得很好的效果,为此使用具有平移不变性及更多方向选择性的双树复小波变换图像融合算法,通过选择适当的融合规则,使乘性和加性噪声模型优势互补,就能有效抑制斑点噪声。实验结果显示,这种消噪方法与其他方法相比,有明显优势。     

基于FRFT的非线性调频信号双特征检测方法

强噪声中的未知非线性调频信号检测一直是实际检测领域中的一个难点问题.非线性调频信号的相位历史是关于时间的未知平滑函数,并且该函数不能或者很难用少量参数来建模.由于相位模型的缺失,我们提出一种基于接连分数阶Fourier变换和双特征检测的非参数化检测方法.检测方案包含3部分:首先,计算一个周期内接连角度的分数阶Fourier变换,将这些不同角度的变换结果构造成二维图像;然后,利用硬阈值处理获得二值图像,并利用多重中值滤波来去除该二值图像中的孤立噪声点,从而获得精炼的二值图像;最后,根据精炼图像提取2个互补的检测统计量,并通过双特征检测器判决目标是否存在.本文针对3类不同阶的多项式相位信号以及正弦相位信号的对比实验结果表明,提出的方法是有效且鲁棒的,并且获得了满意的检测性能.     

SAR图像的改进相位相关配准方法

基于相位相关的图像配准方法以其运算量小、抗干扰性强等优点得到广泛关注,但它只能解决具有平移运动的图像配准问题,为此,文章提出一种改进的相位相关配准方法,分析了其配准原理,给出流程图,并将其应用于存在缩放、旋转和平移变换的SAR图像配准中。仿真结果表明,该算法能够较为准确地估计出待配准图像相对于基准图像的平移参数、缩放尺度和旋转角度。     

Registration of translated and rotated images using finite fourier transforms

A well-known method for image registration is based on a conventional correlation between phase-only, or whitened, versions of the two images to be realigned. The method, covering rigid translational movements, is characterized by an outstanding robustness against correlated noise and disturbances, such as those encountered with nonuniform, time varying illumination. This correspondence discusses an extension of the method to cover both translational and rotational movements.     

Estimation of object motion parameters from noisy images

An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.     

A frequency domain technique for range data registration

This work introduces an original method for registering pairs of 3D views consisting of range data sets which operates in the frequency domain. The Fourier transform allows the decoupling of the estimate of the rotation parameters from the estimate of the translation parameters, our algorithm exploits this well-known property by suggesting a three-step procedure. The rotation parameters are estimated by the first two steps through convenient representations and projections of the Fourier transforms' magnitudes and the translational displacement is recovered by the third step by means of a standard phase correlation technique after compensating one of the two views for rotation. The performance of the algorithm, which is well-suited for unsupervised registration, is clearly assessed through extensive testing with several objects and shows that good and robust estimates of 3D rigid motion are achievable. Our algorithm can be used as a prealignment tool for more accurate space-domain registration techniques, like the ICP algorithm.     

A Frequency Domain Technique Based on Energy Radial Projections for Robust Estimation of Global 2D Affine Transformations

The contribution of this paper is twofold: (1) it provides a thorough analysis of the frequency domain relationships relating two affine-warped images and (2) based on a fundamental equation between energy radial projections, it presents an original algorithm for estimating the global 2D affine transformation between the two images. It is well known that operating in the frequency domain allows one to separate the estimate of the affine matrix, related to the magnitudes of the Fourier transforms of the two images, from the estimate of the translation vector, related to their phases. Exploiting this property, our algorithm consists of two main steps: (1) the affine matrix is first estimated by solving, with a coarse-to-fine strategy, a suitable minimization problem formulated upon the radial projections of the image energies, and (2) after compensation for the contribution of the affine matrix, the translation vector is then recovered by means of phase correlation. The proposed method is very robust against perspective distortion and, with moderate translational displacements, it may also work when the two images differ along their peripheral areas. Experimental evidence of these characteristics is reported and discussed. The algorithm can be efficiently implemented via FFT and well suits applications requiring unsupervised and/or quasi-real-time estimation of global motion that can be described with 2D affine transformations.     

Estimating the kinematics and structure of a rigid object from asequence of monocular images

The problem considered involves the use of a sequence of noisy monocular images of a three-dimensional moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be smooth. A set of object match points is assumed to be available, consisting of fixed features on the object, the image plane coordinates of which have been extracted from successive images in the sequence. Structure is defined as the 3-D positions of these object feature points, relative to each other. Rotational motion occurs about the origin of an object-centered coordinate system, while translational motion is that of the origin of this coordinate system. In this work, which is a continuation of the research done by the authors and reported previously (ibid., vol.PAMI-8, p.90-9, Jan. 1986), results of an experiment with real imagery are presented, involving estimation of 28 unknown translational, rotational, and structural parameters, based on 12 images with seven feature points     

Cloud masking of MODIS imagery based on multitemporal image analysis

We present multitemporal Bayesian classification of Moderate Resolution Imaging Spectroradiometer (MODIS) mosaic data over the Caspian Sea during winter. The multitemporal analysis methods used were cross-correlation and motion detection based on phase correlation. Our motion estimation algorithm estimates the motion of a target between two adjacent images over the same areas based on finding the maximum correlation with respect to location shift within a given area around each location. The motion detection algorithm also provides a quality estimate for the detection. Because sea ice, unlike clouds, is typically rigid and its motion is significantly slower than the motion and metamorphosis of clouds, drifting sea ice can be distinguished from clouds. Over land and static sea ice, detection of clouds, is easier because the cross-correlation is typically higher for land and ice than for clouds. Also, locating clouds over open water is straightforward because clouds appear significantly brighter than open water. The results show that multitemporal features can be used to distinguish between clouds and clear sky.     

摄像机简化模型对三维重构的影响--分析与实验

讨论了摄像机简化模型对三维重构的影响．主要结论有：当摄像机在两幅图像间的运动为纯平移运动时,从理论上证明了使用摄像机简化模型重构空间点与实际空间点之间满足仿射变换;当摄像机在两幅图像间的运动为一般刚体运动时,使用简化模型的重构只有在一定条件下才能较好地保持原物体的形状;在简化模型下,基于Kruppa方程的方法所估计的焦距精度不能满足三维重构的要求．实验结果表明：在三维重构中不能盲目地使用简化模型,必须对摄像机内参数进行全面标定．     

3-D translational motion and structure from binocular image flows

Image flow fields from parallel stereo cameras are analyzed to determine the relative 3-D translational motion of the camera platform with respect to objects in view and to establish stereo correspondence of features in the left and right images. A two-step procedure is suggested. In the first step, translational motion parameters are determined from linear equations the coefficients of which consist of the sums of measured quantities in the two images. Separate equations are developed for cases when measurements of either the full optical flow or the normal flow are available. This computation does not require feature-to-feature correspondence. In addition, no assumption is made about the surfaces being viewed. In the second step of the calculation, with the knowledge of the estimated translational motion parameters, the binocular flow information is used to find features in one image that correspond to given features in the other image. Experimental results with synthetic and laboratory images indicate that the method provides accurate results even in the presence of noise     

基于相位相关的指纹拼接方法研究

基于指纹图像的旋转、平移、距离等不变特性,从增加指纹图像的有效特征点出发,提出了指纹拼接方法。该方法分为三步：第一步,提出基于中值滤波及局部属性的归一化预处理算法,去除图像噪声和影响相位相关度准确性的频率信息;第二步,图像进行伪极坐标快速傅立叶变换,根据旋转和平移的不变性,利用相位相关对图像进行快速配准;第三步,对配准的指纹图像完成平滑拼接。大量的实验结果证明本方法可以增加有效的指纹图像特征点数量,改善比对效果,计算复杂度较低,可以实现指纹图像的快速拼接。     

基于Gaussian-Hermite矩的旋转运动模糊不变量

目的 模糊图像的分析与识别是图像分析与识别领域的重要方向。有些图像形成过程中成像系统与物体之间存在相对旋转运动,如因导弹高速自旋转造成的制导图像的旋转运动模糊。大多数对于这类图像的识别都需要先对模糊图像进行“去模糊”的预处理,且该类方法存在计算时间复杂度较高及不适定的问题。对此,提出一种直接提取旋转运动模糊图像中的不变特征,用于旋转运动模糊图像目标检索和识别。方法 本文以旋转运动模糊的退化模型为出发点,提出了旋转运动模糊Gaussian-Hermite(GH)矩,构造了一组由5个对旋转变换和旋转运动模糊保持不变性的GH矩不变量组成的特征向量(rotational motion blur Gaussian-Hermite moment invariants, RMB＿GHMI-5),可从旋转变换和旋转运动模糊的图像中直接进行目标检索和识别,无需前置复杂的“去模糊”预处理过程。结果 在USC-SIPI(University of Southern California—Signal and Image Processing Institute)数据集上进行不变性实验,对原图进行不同程度的旋...