数学形态学在作物病害图像处理中的应用研究

作为一种2维卷积运算的非线性图像处理方法,数学形态学的内容包括二值形态学、灰度形态学和彩色形态学。膨胀、腐蚀、开运算、闭运算是数学形态学的基础。数学形态学可用于噪声去除、边缘检测、图像分割、特征提取等图像处理问题,在图像处理领域得到了越来越广泛的应用。结合目前的研究进展,对数学形态学的分类及其在作物病害图像处理中的应用进行综合性阐述,并对数学形态学目前存在的问题以及未来的发展方向进行了讨论。     

大结构元素二值形态学基本操作改进算法

二值图像的数学形态学方法应用广泛,但当涉及的图像和结构元素较大时,操作速度变慢。针对结构元素参考点包括在结构元素中且为单一连通区域的大结构元素,提出了二值形态学膨胀操作的改进算法,首先提取待膨胀二值区域的轮廓,然后对轮廓进行膨胀,再将膨胀结果与原二值区域取并集得到总的膨胀结果;证明了改进膨胀算法与标准膨胀操作的等价性;基于膨胀与腐蚀操作的对偶关系给出了改进的腐蚀算法;给出了改进的开、闭运算算法。在80张高分辨率植物叶片二值图像上进行了腐蚀、膨胀、开运算和闭运算标准方法和改进算法的对比实验,结果表明改进算法可显著提高二值形态学处理的速度。     

基于数学形态学与Hough变换的乐谱谱线探测算法研究

数学形态学是综合了多学科知识的交叉学科,是一种非线性的图像分析理论,己成为图像处理的重要工具之一。文章简单介绍了数学形态学和二值形态学的基本运算—腐蚀和膨胀,并提出了基于数学形态学的乐谱谱线探测算法。实验结果证明,与Hough变换探测直线算法相比,该乐谱谱线探测算法具有运算速度快、效率高、抗噪声能力强等优点。     

基于细胞神经网络的数学形态滤波方法与应用

数学形态学以集合运算为基础,在图像处理领域得到了广泛地运用。数学形态学以集合运算为基础,用具有一定形态的结构元素去度量图像中的形态以解决理解问题。证明利用细胞神经网络（Cellular Neural Network）,运用数学形态滤波可并行完成数学形态运算。该文给出了细胞神经网络（CNN）在腐蚀、膨胀、结构开、结构闭中的实现及应用。将其结果运用在指纹图像的预处理当中,取得了较理想的结果。     

数学形态学运算的再定义及其神经网络实现

该文在分析了传统的数学形态学基本运算的基础上,利用集合势的概念,提出了一种新的形态学算子定义方法,并据此引入了程度化数学形态学运算的概念,将腐蚀和膨胀运算合二为一。最后讨论了程度化形态学运算的神经网络实现及计算机仿真结果,从而,在不同于前人思路的情况下,实现了使用较少的结构元素信息对图象进行噪声滤波的操作。     

用数学形态学进行图像边缘检测的新方法

本文提出了求灰值腐蚀、膨胀运算结果的简便方法--模板法.应用此方法在检测灰度图像的边缘过程中可较容易地做腐蚀、膨胀运算.先得出用数学形态学检测灰度图像边缘的算法1,然后在分析了其效果并指出不足的基础上,把算法1中用一个结构元素既做腐蚀又做膨胀,改进为用四个不同方向的结构元素只做膨胀,得出了效果比较理想的算法2.     

数学形态学在图象处理中的应用进展

数学形态学是一种非线性滤波方法,形态和差运算,即膨胀与腐蚀是数学形态学的基础,数学形态学已由二值形态学、灰度形态软数学形态学、模糊形态学发展到模糊软形态学,可用于抑制噪声、特征提取、边缘检测、图象分割、形状识别,纹理分析、图象恢复与重建等图象处理问题,在图象处理领域得到了越来越广泛的应用,本文结合目前的研究进展,对数学形态学的理论研究及其应用进展进行综述性阐述。     

基于数学形态学的烟支计数识别

根据烟支的几何特征,设计1种基于数学形态学的烟支图像分割并计数识别方法．该方法对二值图像应用形态滤波器去噪,对单个标记区域进行形状分类并使用不同的结构元素进行开运算,在腐蚀后膨胀前进行单烟支判定,达到分类处理的效果．在整个形态处理过程中引入形态编码概念,从而将形态学运算转换为字节中的位运算,同时将二值图中的区域转换为整数集合,作为1种区域描绘应用在单烟支判定中．     

边缘检测的形态学算法与传统算法比较

由于图像的边缘通常含有大量重要信息,因此,边缘检测成为图像处理的一个重要环节,其检测算法也获得了广泛的研究,已经形成了Roberts、Laplacian、Canny等多种算法。但这些传统算法在边缘检测精度和抗噪声性能方面还存在一定的问题。文章运用数学形态学边缘检测算法的结构元素变换,对无噪声图像检测出多幅边缘图;对噪声图像采用改进的开启运算,先用3×3的结构元素进行腐蚀,后用5×5的结构元素进行膨胀,用边缘检测算子f°B-f进行检测,并与传统算法和不变结构元素的形态学开启运算的结果进行了比较。实验结果表明,灵活多变的数学形态学边缘检测算法在检测精度和抗噪声性能上都优于传统算法。     

基于数学形态学的突发性故障快速定位方法

针对于突发性故障的特点,提出了一种基于数学形态学的故障定位方法,通过选择适当的结构元素,根据腐蚀和膨胀运算的特点设置适当的组合形态运算,借鉴软阈值计算中的消噪方法,设计高效的故障定位算法,仿真实验证明了数学形态学在故障定位中所具有的优越性。     

Locally adaptable mathematical morphology using distance transformations

We investigate how common binary mathematical morphology operators can be adapted so that the size of the structuring element can vary across the image pixels. We show that when the structuring elements are balls of a metric, locally adaptable erosion and dilation can be efficiently implemented as a variant of distance transformation algorithms. Opening and closing are obtained by a local threshold of a distance transformation, followed by the adaptable dilation.     

用形态学边缘检测算子实现图像的浮雕显示

提出了一种以数学形态学为基础进行图像浮雕显示的新方法，该方法利用数学形态学的非线性处理能力和擅长处理图像几何结构的优势，在其基本的形态学腐蚀差和膨胀差边缘检测算子中使用特殊结构的结构元获得图像的浮雕显示效果。该方法原理简单，易于实现。其浮雕显示实验表明，与使用广义模糊算子实现的图像浮雕显示方法相比，本文所提方法能获得更为满意的浮雕显示效果，且计算的时间耗费大大降低。     

Fuzzy mathematical morphology

A new morphology is proposed which uses fuzzy structuring elements and is internal on fuzzy sets. It is fully compatible with conventional morphology which uses binary structuring elements, either on binary or on grey-tone sets. The properties of the two basic operations, fuzzy dilation and fuzzy erosion, are presented. An example showing the interest of fuzzy morphology to manipulate the uncertainty linked to spatial information is presented in multisource medical image data fusion.     

Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology

Bridges over water are typical man-made structures on the land’s surface. An accurate extraction of such bridges from high-resolution optical remote-sensing images plays an important role in civil, commercial, and military applications. Considering the complex features of ground objects within high-resolution optical remote-sensing images and the inefficiency of previous methods of bridge extraction with random bridge orientation, direction-augmented linear structuring elements were constructed and applied in this study by using mathematical morphology to identify and extract bridges over water with different orientations. First, the image pre-processing is performed to facilitate the object extraction. Then by using the histogram-based threshold segmentation method, water bodies such as rivers are extracted and described as a binary image. Based on water bodies, the appropriate direction-augmented linear structuring element is then selected. Together with mathematical morphology operations, such as dilation and erosion, potential bridges are extracted by overlay analysis. Assisted by prior knowledge of bridges, false bridges are screened out and post-processing is finally performed to refine the extracted true bridges. This approach was validated with experiments in Shanghai and Beijing, China. The results show that the direction-augmented linear structuring elements are of high precision and have the capability of extracting bridges over water in different directions within the high-resolution optical remote-sensing image, considering both qualitative and quantitative aspects. Therefore, this approach may be useful in updating geographical databases of bridges and facilitating the assessment of bridge damage caused by natural disasters.     

Speeding-up successive Minkowski operations with bit-plane computers

Performing successive Minkowski operations on binary images is a well known and widely used task in image processing. In bit-serial parallel computers (so called bit-plane computers) the time necessary to perform such operations depends to a great extent on the complexity of the particular structuring element T. As it is well known, this computation time can be reduced if T is decomposed into the (set theoretical) sum of simpler structuring elements. Such decompositions, however, are known only for a very narrow class of structuring elements. In this paper, a modification of that decomposition method is presented which results in speeding up the Minkowski operations for a broader class. It is shown that, after a certain number of steps, just the ‘extreme points’ of the structuring element are important. So, unlike the convenient methods, it is successfully applied only if sequences of Minkowski operations are applied. This is the case in particular in mathematical morphology when erosion, dilatation, opening (ouverture), and closing (fermeture) are performed repeatedly.