一种基于数学形态学的遥感图象边缘检测算法

为有效提取边缘密度大的遥感图象中的边缘信息,该文提出了一种利用灰度形态变换原理进行检测的有效算法,首先利用开-闭形态滤波器降低输入图象的噪声,然后将二值图象的边缘提取算法推广到灰度图象中加以应用。实验结果证明,此方法优于灰度形态梯度法,同时也优于Canny算子。     

一类新的广义形态开和广义形态闭滤波器

以数学形态学为基础的形态滤波器是一种非线性滤波器,基于基本的形态变换—腐蚀和膨胀,采用大小不同的结构元素,提出了一类新的形态滤波器——广义形态开和广义形态闭滤波器,这类滤波器具有平移不变性、递增性、对偶性等重要性质。为了验证广义形态开、闭滤波器的有效性,针对不同的噪声,使用这类滤波器对其进行滤波,滤波结果表明广义形态开、闭滤波器在抑制噪声的同时能够较好地保持细节,而且运行的时间将小于形态开闭、闭开、广义形态开-闭、闭-开滤波器运行的时间,形态滤波器算法简单便于并行实时处理。     

基于自适应堆滤波的边界检测

从被噪声干扰的图象中提取边界是图象测试与分析的关键之一。通常需要先滤除图象中的噪声，再用边界检测算子求出边界。本文介绍了一种边界直接检测法，即将边界检测与噪声滤波相结合，它是基于自适应堆滤波的边界检测法。首先非线性堆滤波器用于求出图象某象素点邻域内的灰度最大值与最小值的最优估计，然后以此两估计值之差代替原象素点灰度值。最后对之二值化求出边界。本文根据最小平均绝对误差准则，采用自适应方法求解堆滤波器。这种方法类似于线性自适应滤波器的ＬＭＳ方法，先任设一初始堆滤波器，利用期望图象与合噪声图象对堆滤波器进行迭代训练，最后求出最优化的自适应堆滤波器。文章最后给出了采用自适应堆滤波法求取图象边界的试验结果，表明这种方法可以有效地抑制各种分布的噪声干扰。     

基于形态开闭滤波二值标记和纹理特征合并的分水岭算法

为了防止分水岭算法过分割问题 ,研究了一种基于形态处理和纹理分析的图象分水岭分割方法 ,该方法是在分水岭算法的基础上 ,首先对形态梯度图象运用形态开闭滤波器组的方法来获得较好的参考图象 ,然后将提取的二值化初始分割结果作为区域标记来进行分割 .另外 ,为了获得整体目标 ,还定义了一个基于纹理特征的一致性和对比度的检验准则 ,并将其作为区域合并的根据 .该方法应用于多个视频序列初始目标分割的结果表明 ,形态滤波器组的引入很好地防止了过分割 ,并证明基于纹理特征均匀性和对比度的准则对分割区域进行进一步的纹理合并是行之有效的 .     

基于直方图的图象去噪滤波器

滤除图象噪声时，虽然利用的先验知识越多，其滤波效果越好，但是一般情况下，由于只能得到一幅被污染的图象，无法获得这些先验知识，因而滤波效果较差。为了解决该问题，提出了一种去除图象中椒盐噪声的新型滤波器。该滤波器首先给出了一种有效的估计原图象直方图的方法，进而利用估计直方图的信息来进行滤波；然后对滤波窗口中的像素进行一种排除最大和最小灰度值的操作，以滤除椒盐噪声点。实验表明，该方法滤波效果优于传统的滤波器和其他模糊滤波器，特别是当噪声概率超过0．3时，这种优势尤为明显。     

SAR图象中条纹干扰的抑制

针对合成孔径雷达成像中特有的条纹干扰,分析了干扰对图象频谱的影响,提出了利用方向滤波器去除ＳＡＲ图象中条纹干扰的方法。并从理论上证明了该方法的科学性,给出了方向滤波器的算法步骤。实验结果显示,用方向滤波器算法处理图象,可有效去除干扰,保持图象细节不受损失。     

全方向增强局域统计自适应滤波器在平滑雷达图象斑点噪声中的应用

合成孔径雷达图象的斑点噪声是由于合成孔径雷达（SAR）发射的相干干涉波产生的，它严重地影响了合成孔径雷达图象的质量和应用。为了更好地平滑噪声，保护边界和纹理信息，本文在分析和实现适应于SAR图象的各种局域自适应滤波器的基础上，实现了一种新的改进的全方向增强的局域自适应滤波器，它是把（2N＋1）＊（2N＋1）的滤波子窗口分成4N个子窗口，根据不同的子窗口中的局域变化系数来进行滤波，结果表明，这种全方向增强的局域自适应滤波器在保护地物的辐射特性和图象的纹理信息以及增强图象的微细节等方面，均比以前的滤波器有较大的提高。     

基于Facet模型产生多分辨率图象锥

提出了一种产生多分辨率图象锥的新方法。它基于Ｆａｃｅｔ模型在最小二乘法的准则下，把图象的灰度函数用二维平面拟合，达到直接在二维空间滤波的目的，而不同于以往的先在一维空间产生滤波器，再去近似二维空间滤波器的方法。实验结果表明，该方法比现有的其它产生多分辨率图象锥的方法具有信息能量损失小的优点。     

数学形态滤波在振动信号分析中的应用研究

本文介绍了数学形态滤波的基本原理与形态滤波器的构建。针对环境试验振动信号中存在的非线性噪声问题,我们提出一种基于开、闭运算的级联组合数学形态滤波器的新方法。通过将数学形态滤波器用于复杂锥筒结构振动信号中的脉冲干扰的实例分析与运算,说明该方法对振动信号的干扰噪声有很好的滤波效果,亦表明数学形态滤波有很好的的应用前景。     

比较线性相位完全重构滤波器阵列在图象编码中的应用

比较了两种不同类型的多通道线性相位完全重构滤波器阵列在图象编码中的应用。由于图象的绝大部分能量集中低于频端,我们发现低频端滤波器的长度大于高频端滤波器长度的线性相位完全重构滤波器阵列比各个通道滤波器长度相同的滤波器阵列有较 编码效果。     

双算子形态学滤波器

传统的形态学滤波算子交替性差、耗时长且抑制噪声能力弱. 基于中心互补结构元素与交替对偶算子, 提出了双算子形态学滤波器. 该滤波器继承了经典形态学滤波器的递增性、对偶性和幂等性, 但不满足扩展性和非扩展性. 双算子形态学滤波器具有离散的邻域运算特性, 采用交替小结构元素能去除较结构元素大的噪声块, 且在抑制噪声的同时有效保留了图像细节. 实验结果表明, 与基本的形态学滤波器及目前已改进的形态学滤波器相比, 双算子形态学滤波器具有更强的噪声抑制性能, 且在同等滤波效果下, 其计算量更小, 最终滤波后的图像具有较高的峰值信噪比和较小的均方根误差.     

Implementation of morphological filters using coordinate logic operations

In this paper we present a methodology for performing morphological operations using coordinate logic operations. Coordinate logic operations are simple and fast because they are logic operations among the corresponding binary values of the image pixels. Coordinate logic (CL) filters are a family of fast non-increasing, non-linear filters, which are based on coordinate logic operations. CL filters exhibit analogous properties to those of the morphological filters but due to their different definition they display slightly different response. This is revoked in this paper by using appropriate image gray level quantization and mapping of the resulting levels to a specific set of decimal numbers. Based on this approach some fast efficient implementations of morphological filters using CL operators are proposed.     

Automatic target detection by optimal morphological filters

It is widely accepted that the design of morphological filters,which are optimal in some sense,is a difficult task.In this paper a novel method for optimal learning of morphological filtering parameters(Genetic training algorithm for morphological filters,GTAMF)is presented.GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutaition to achieve optimal filtering parameters in a global searching,Experimental results show that this method is practical,easy to extend,and markedly improves the performances of morphoological filters.The operation of a morphological filter can be divided into two basic problems including morphological operation and structuring element (SE)Selection.The rules for morphological operations are predefined so that the filter‘s properties depend merely on the selection of SE.By means of adaptive optimization training,structureing elements possess the shape and structural characteristics of image targets,and give specific information to SE.Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.     

Extraction of neurons from images of mouse brain slices based on automated selection of connected morphological filters

We describe the results of a study on creating an algorithm for automated selection of connected morphological filters to solve image segmentation problems. We propose a similarity measure for image partition. It is used to select the best filters from given families of connected morphological filters in such a way that partition obtained by applying the watershed-segmentation algorithm to a filtered image has the maximum similarity value with the given partition. This method is used to extract neurons from images of mouse brain slices. Experimental research has confirmed that this method is applicable for automated processing and analysis of slice images.     

Soft Morphological Filtering Using a Fuzzy Model and its Application to Colour Image Processing

Abstract Soft morphological filters form a class of filters with many desirable properties. They were introduced to improve the behaviour of standard morphological filters in detail preservation and noise elimination. In this paper, a framework for soft morphological colour image processing using a fuzzy model is introduced. This extends the standard colour morphological operators in the same way that soft greyscale morphology extends the standard greyscale morphology theory. The primary and secondary operations of the new soft morphological approach are defined. The proposed operators are less sensitive to image distortion and to small variations in the shape of the objects, and perform significantly better in impulse noise removal problems, compared to standard morphological operators. Experimental results of the application to real colour images demonstrate these advantageous characteristics of the new operators. Additionally, illustrative examples that exhibit the applicability of the proposed methodology to edge detection problems are also included.An erratum to this article can be found at     

自动检测图像目标的形态滤波遗传算法

提出了一种实现形态滤波器参数优化设计的贵传学习算法（Genetic Training Algorithm for Morphologi-cal Fitters,GTAMF）。采用新的交叉与变异算子－曲面体交叉与主从式变异,通过优化搜索全局以获得滤波性和时效性兼优的形态滤波器参数。实验结果表明该方法设计方便,实用性强且易于推广,对提高形态滤波性能效果明显,分析表明,形态滤波器可分解为形态学运算和结构元选择两个基本问题,形态学运算的规则已由定义本身而确定,于是形态滤波器的最终滤波性能就仅仅取决于结构元的选择。通过自适应优化训练使结构元具有图像目标的形态结构特征,从而赋予结构元特定的知识,使形态滤波过程融入特有的智能,以实现对复杂变化的图像具有良好的滤波性能和稳健的适应能力。     

利用非线性滤波进行多尺度拐角检测

对灰度图进行一般线性和非线性平滑会极大程度地模糊拐角,使得多尺度方法难以用于拐角检测.由于非线性多尺度CB(contour-based)形态滤波算子具有很好的拐角保持特性,而且能使拐角满足单调性,即当尺度增大时拐角数不增加.用CB形态滤波器对灰度图进行滤波,构成CB形态学尺度空间,以实现对拐角的多尺度检测.理论分析和实验结果证明了这种分析方法的有效性.     

一类新的广义形态学混合滤波器

传统形态学滤波器滤波后的图像灰度值产生偏移,滤波算子抑制噪声效果较差.基于双路对偶形态学算子的混合滤波器解决了传统形态学滤波器存在的灰度值偏移问题,但运算次数多,处理速度较慢.提出了广义混合交变序列滤波器,该滤波器继承了一般混合交变序列滤波器的重要性质,保持了对偶滤波器的强对称性,且运算量减少了一半.仿真实验表明,该混...     

Optimal structuring elements for the morphological patternrestoration of binary images

In this paper, we derive the optimal structuring elements of morphological filters in image restoration. The expected pattern transformation of random sets is presented. An estimation theory framework for random sets is subsequently proposed. This framework is based on the least mean difference (LMD) estimator. The LMD estimator is defined to minimize the cardinality of the expected pattern transformation of the set-difference of the parameter and the estimate. Several important results for the determination of the LMD estimator are derived. The LMD structuring elements of morphological filters in image restoration are finally derived     

A representation theory for morphological image and signalprocessing

A unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis is presented. The unification requires a set-theoretic methodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translational-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-invariant, increasing, upper semicontinuous system can be presented exactly as a minimal nonlinear superposition of morphological erosions or dilations. The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic filters, linear filters, and shape recognition transforms. Although the developed theory is algebraic, its prototype operations are well suited for shape analysis; hence, the results also apply to systems that extract information about the geometrical structure of signals