基于特征矢量匹配的SAR海洋图像舰船目标检测

根据SAR图像的基本特点 ,阐述了用DFBR场模型表达SAR图像的基本原理。在此基础上 ,通过提取SAR海洋图像中每个像素的分形值、分形模型拟合误差和方差统计量特征参数 ,结合图像像素的灰度值 ,形成一个对应于每个像素的特征矢量 ,并利用模糊数学的模糊子集概念 ,提出了一种基于特征矢量匹配的舰船目标检测方法。实际数据处理的结果表明 ,该方法具有较高的可靠性和准确性。     

分形方法用于有噪图像边缘检测的研究

本文研究了加性高斯白噪声对于基于离散分数布朗随机场模型图像分形维数估计的影响，并将分形方法用于图像边缘检测，指出在加性高斯白噪声的情况下，分形方法用于图像边缘检测较之经典的基于梯度运算的边缘检测方法有好的抗噪性能，同时又能检测比较丰富的图像边缘细节。     

A maximum likelihood estimate for two-variable fractal surface

The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelihood estimate (MLE) is developed. We formulate a model to describe the two-variable fractional Brownian motion, then derive the likelihood function for that model and estimate the fractal dimension by maximizing the likelihood function. We then compare the MLE with the box-dimension estimation method.     

一种基于局部分形维的CFAR检测算法

目标检测是图像处理领域和计算机视觉中一项非常重要的研究课题.针对光学遥感图像自然背景下人造目标检测中检测时间长,虚警率偏高的问题,本文提出一种基于局部分形维的CFAR检测算法.该算法首先引入重标极差分析法,把图像的局部窗转化为一维序列的形式,且通过对一维序列极差和偏差的运算得到反映图像局部纹理特征的局部分形维,并以此构造出图像的分维像.然后在分维像基础上进行快速CFAR检测,确定滑窗中心点像素是否为目标像素.最后对目标像素进行聚类以提取感兴趣目标区域.利用本文提出的算法对不同地区的光学图像进行了大量的实验,得到了较好的检测结果.实验结果证明了该算法在高分辨光学图像中能有效、快速地地检测自然背景中的人造目标.与传统的人造目标检测算法相比,本文提出的算法能有效地减少检测时间,降低虚警率.     

基于分形特征的红外烟幕遮蔽热像效果评价

提出一种基于分形特征的红外烟幕对目标热像遮蔽效果的评价方法,该方法将烟幕运动视为分形布朗运动,以自然背景和人造目标图像在分形特征上的固有差异为依据,利用战术目标分形拟合误差大的特点,采用表面积法计算目标热像区域不同时刻分维数和分形拟合误差的变化,来评价烟幕对目标热像遮蔽的程度。实验结果表明该评价方法有助于对遮蔽程度作出准确判别与分析。     

Fractal dimension estimation via spectral distribution function and its application to physiological signals

Rhythmic signals from physiological systems usually have memory and long-term correlation. They can be modeled as fractional Brownian motion or fractional Gaussian noise depending on if the signals are derived from cumulative effects of nerves and muscles. That is, they can be treated as signals with fractional dimension, and the value of its fractal dimension can be used to characterize the intensity of physiological signals. In this communication, a novel method of dimension estimation based on the calculation of spectral distribution function of discrete-time fractional Gaussian noise using Legendre polynomials as basis set is proposed. The effectiveness of this proposed method is demonstrated in the dynamic behavior of detrusor of the bladder and external urethral sphincter during micturition.     

非局部多尺度分数阶微分图像增强算法研究

为了更好增强图像中的有用信息,改善图像视觉效果,该文提出了一种基于非局部多尺度分数阶微分图像增强算子(NMFD)。该算子首先将图像分成若干块子图像,计算每一块子图像的边缘强度系数、熵值和粗糙度等细节特征,将得到的特征数据在全局图像范围进行统一尺度的归一化,然后对这些归一化的数据进行加权求和作为图像的非局部特征值,最后利用指数函数建立图像细节特征和分数阶微分算子阶次之间的非线性量化关系,在不同的图像子块区域,确定不同尺度的分数阶微分阶次,实现图像的非局部多尺度增强。     

A Power Differentiation Method of Fractal Dimension Estimation for 2-D Signals

Fractal dimension has been used for texture analysis as it is highly correlated with the human perception of surface roughness. Several methods have been proposed for the estimation of the fractal dimension of an image. One of the most popular is via its power spectrum density, provided that it is modeled as a fractional Brownian function. In this paper, a new method, called the power differentiation method (PDM), for estimating the fractal dimension of a two-variable signal from its power spectrum density is presented. The method is first applied to noise-free data of known fractal dimension. It is also tested with noise-corrupted and quantized data. Particularly, in the case of noise-corrupted data, the modified power differentiation method (MPDM) is developed, resulting in more accurate estimation of the fractal dimension. The results obtained by the PDM and the MPDM are compared directly to those obtained using four other well-known methods of fractal dimension. Finally, preliminary results for the classification of ultrasonic liver images, obtained by applying the new method, are presented.     

Using vector quantization for image processing

A review is presented of vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, which is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the compression step. Recent work has used vector quantization both to simplify image processing tasks, such as enhancement classification, halftoning, and edge detection, and to reduce the computational complexity by performing the tasks simultaneously with the compression. The fundamental ideas of vector quantization are explained, and vector quantization algorithms that perform image processing are surveyed     

Texture features for classification of ultrasonic liver images

The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images.     

Estimation of 2-D noisy fractional Brownian motion and itsapplications using wavelets

The two-dimensional (2-D) fractional Brownian motion (fBm) model is useful in describing natural scenes and textures. Most fractal estimation algorithms for 2-D isotropic fBm images are simple extensions of the one-dimensional (1-D) fBm estimation method. This method does not perform well when the image size is small (say, 32x32). We propose a new algorithm that estimates the fractal parameter from the decay of the variance of the wavelet coefficients across scales. Our method places no restriction on the wavelets. Also, it provides a robust parameter estimation for small noisy fractal images. For image denoising, a Wiener filter is constructed by our algorithm using the estimated parameters and is then applied to the noisy wavelet coefficients at each scale. We show that the averaged power spectrum of the denoised image is isotropic and is a nearly 1/f process. The performance of our algorithm is shown by numerical simulation for both the fractal parameter and the image estimation. Applications to coastline detection and texture segmentation in a noisy environment are also demonstrated.     

一种多尺度分形的舰船目标检测方法

阐述了图像分形维和多尺度分形特征的概念,提出了一种多尺度分形目标检测方法。实验证明,自然背景与人造目标所在区域的多尺度分形特征差别非常明显。对于舰船目标检测,给出方法的检测结果稳定,优于边缘检测、阈值分割和单一分形维检测的结果,是一种非常有前途的方法。     

Fractal-based classification of natural textures

Texture classification is an important first step in image segmentation and image recognition. The classification algorithm must be able to overcome distortions, such as scale, aspect and rotation changes in the input texture. In this paper, a new fractal model for texture classification is presented. The model is based on fractional Brownian motion (FBM). It is also shown that this model is invariant to changes in incident light; empirical results are also given. The isotropic nature of Brownian motion is particularly useful for outdoor applications, where the viewing direction may change. Classification results of this model are presented; comparisons with other texture measurement models indicate that the incremental FBM (IFBM) model has better performance for the samples tested     

一种基于多重分形的SAR图像边缘检测方法

分形维数只能刻画那些具有理想的自相似性的分形体,现实中的许多纹理并不满足这一条件,因此单一的分形维数并不足以描述和刻画SAR图像的纹理,多重分形维数更适合于描述图像的纹理.通过计算原始SAR图像离散点数据的奇异性指数,然后对应每一点奇异性指数计算全局多重分形奇异谱,根据判决准则区分边缘和纹理可以实现SAR图像的边缘检测,实验结果表明,基于多重分形特征的边缘检测算法能够检测到许多局部细节,同时又避免出现不重要的细节,突出了主要的边缘信息,很好地区分出SAR图像的纹理和边缘.     

基于多重分形的红外图像增强技术

红外图像边缘模糊,对比度较低,不适合人眼观察,应该对其进行增强.但是,以往的增强方法对噪声增强过度,使细节失真,且未考虑人眼的视觉特性,视觉效果不够好.提出用多重分形理论对红外图像进行分析,提取了红外图像的多重分形奇异指数和多重分形谱特征.分析得到了图像每个像素的分形特征数据,利用人眼的视觉敏感特征把图像的像素分为平滑区、纹理区和边缘区.人眼视觉空间频率特征对图像细节的边缘区域比较敏感,利用这一特性对图像加权增强.最后,进行了计算机仿真实验,实验结果表明:该方法能够突显人眼敏感的图像区域,解决红外图像边缘模糊的问题,使增强图像更适合人眼观察.     

二维fBm随机分形界面的电磁散射特性

应用二维fBm（分形布朗运动）模拟了这种既非完全周期的也非完全随机的实际粗糙界面，用Kirchhoff方法计算了电磁波在随机分形界面的散射特性。数值结果表明随着分形维数的增加，散射场中非相关分量逐渐增大，旁瓣增多并在镜面方向周围不断展宽。     

基于分形特征融合的目标边缘检测算法

针对复杂背景下目标边缘检测问题,研究了分形在目标检测中的应用,提出了基于分形特征融合的目标边缘检测算法。首先求取图像的分形维数和几何度量空间变换率,然后运用Dempster-Shafer证据理论对分形维数和几何度量空间变换率进行融合处理,对图像像素分类,从而得到目标边缘,最后细化边缘得到目标检测结果。实验表明,该算法能够克服复杂背景的干扰,有效地提取目标边缘。     

fBm随机粗糙面电磁散射的微扰法近似

本文采用二维fBm分形函数来模拟二维实际粗糙面,利用微扰法给出了fBm粗糙面的归一化散射截面的计算公式。数值计算了不同分维下的实际粗糙面的后向散射截面,并与有关实验测试结果和高斯相关分布及指数相关分布粗糙面的散射结果作了比较。     

Estimating fractal dimension with fractal interpolation functionmodels

Fractal dimension (FD) is a feature which is widely used to characterize medical images. Previously, researchers have shown that FD separates important classes of images and provides distinctive information about texture. The authors analyze limitations of two principal methods of estimating FD: box-counting (BC) and power spectrum (PS). BC is ineffective when applied to data-limited, low-resolution images; PS is based on a fractional Brownian motion (fBm) model-a model which is not universally applicable. The authors also present background information on the use of fractal interpolation function (FIF) models to estimate FD of data which can be represented in the form of a function. They present a new method of estimating FD in which multiple FIF models are constructed. The mean of the FD's of the FIF models is taken as the estimate of the FD of the original data. The standard deviation of the FD's of the FIF models is used as a confidence measure of the estimate. The authors demonstrate how the new method can be used to characterize fractal texture of medical images. In a pilot study, they generated plots of curvature values around the perimeters of images of red blood cells from normal and sickle cell subjects. The new method showed improved separation of the image classes when compared to BC and PS methods