基于多结构元素的图像边界识别方法研究

在图像处理中,对于复杂图像的边界特征信息很难通过一个结构元素来提取.针对这一问题,采用多结构元素的图像边界识别算法,利用具有视觉模型的边界阀值选择策略确定图像中梯度变化的像素点,对其采用二值形态学的腐蚀运算,从而判断该像素点是边界点还是噪声点.实验表明此算法具有较好的边界信息提取能力和较好的去噪声能力.     

胃上皮肿瘤边界跟踪算法的实现

提出了一种修正型的边界跟踪算法,该算法基于区域标号算法,并以外法线方向作为优先搜索方向来确定边界点,在边界跟踪过程中不但记录目标物体的边界点,而且还将校正像素点的归属区域。针对该算法,设计了一个2维链表的数据结构用来保存跟踪的边界点。     

基于向量模的坐标变换不变性的碎片匹配方法

根据向量模在平移、旋转等坐标变换中具有不变性的特征,给出了一种新的碎片匹配算法。先对碎片图像进行预处理得到图像边界,然后计算每条边界曲线上任意两点构成的向量的模（欧式距离）,并按边界点顺序排列向量模值,比较向量模值,找出最长的匹配段就是两个碎片的匹配边。在拼接时,同样根据模的这一特征,已知点坐标和模值,解方程组即可求得其余边界点在目标坐标系的坐标。该方法原理简明,编程容易,计算速度快。最后通过实验验证了该方法的有效性。     

基于边界点偏置的VORONOI骨架算法的研究

利用Voronoi图求解中轴骨架的方法往往首先将边界用多边形来表示，文中提出将边界点进行偏置，在偏置过程中得到Voronoi图，并得到中轴骨架的算法。这种算法无须对边界进行直线拟合，简单明了，易于实现，适用于任意和任意连通性的二值图像。     

加速基于元球的物体变形方法

为了加速三维物体的变形过程,提出一种基于组件的新方法计算元球的边界体.把每个元球的边界体值存放在数组中,通过查询边界体数组,可以快速求出变形空间内任意一点的影响组件,并计算影响组件的骨架中心到该点的距离以及影响组件相对于该点的所有势函数之和,然后采用移动立方体算法将该点的势函数值与阈值比较,得出变形物体表面顶点的空间坐标值和该顶点的单位法向量.利用OpenGL实现变形物体的绘制过程.     

基于改进边界距离变换的路径提取算法研究*

针对虚拟内窥镜中心路径提取算法时间效率不高的问题,通过改进边界距离变换,提出了中心路径的快速提取算法。首先建立最小距离场,在边界内推过程,仅扫描并处理与边界点面连接的点并对其设标记属性,每次向里剥离一层体素标记属性随之增加,直到对内部点集都置到边界的最小边界距离值（DFB）;然后找到DFB值不小于其所有面邻接点DFB值的体素点,即3D局部最大值;最后用最短路径相连3D局部最大值,形成中心线。实验结果表明,改进的中心路径提取算法的时间效率较传统距离变换算法有很大提高。     

基于广义势场的三维形体多层次线骨架构建

在Cornea建立的基于广义势场的骨架生成算法的基础上,提出一种新的多层次线骨架构建算法。通过选取不同的r值简化边界点,减少了斥力场的计算时间。与Cornea算法不同,选取曲面变分替代曲率,并选取局部曲面变分值较高的边界点作为种子点生成多层次的骨架。由于曲面变分比曲率更适用于反映点云形体表面的性质且计算速度较快,因此该算法更适于处理点云,且具有一定的鲁棒性。同时分析了不同的r值与骨架连通性和计算时间的关系。实验结果表明,经边界点简化处理,斥力场计算时间比原来减少一半左右,且以此生成的骨架能够保持较好的光滑性和连通性。还尝试了另一种基于曲面变分简化边界点的准则,并仔细考查了高曲面变分点、邻域半径k以及不同的空间划分尺度n对多层次骨架生成的影响。     

基于统计信息聚类边界的不平衡数据分类方法

为解决不平衡数据在传统处理方法中容易出现数据的过拟合和欠拟合问题,提出基于统计信息聚类边界的不平衡数据分类方法.去除数据中噪声点,根据数据对象的k距离设定邻域半径,利用对象邻域范围内的k距离统计信息寻找边界点与非边界点;将少数类中的边界点作为样本,采用SMOTE算法进行过采样,对多数类采用基于距离的欠采样删除远离边界的点,得到平衡数集.通过实验结果对比,验证了该算法的G-mean值与F-value值都有提高.     

一种新的完全欧氏距离变换算法

论文提出了一种基于边界剥离的二维完全欧氏距离变换算法。该算法从物体目标的最外层边界开始,自外向内、逐层对物体目标区域进行边界跟踪、剥离。在跟踪过程中,根据当前边界像素点的已获得距离变换结果或为背景的邻域像素信息,计算其与最近背景像素间的欧氏距离,从而实现距离变换。和已有算法相比,文中算法具有简单快速、容易实现,得到的是完全欧氏距离的优点,在分离粘连物体的应用中,取得了良好分离效果。     

切换系统多面体区域的生存性判别

本文研究了切换系统关于多面体区域的生存性判别问题.考虑多面体由有限点集凸包来表示,利用非光滑分析理论,得到一个切换系统生存性的充分条件.该条件只需检验在极点处是否满足特定条件,而不需要对每个边界点进行验证.其优点在于将生存性的判别转化为向量内积与切锥的计算.这种生存性判别方法简便易行.最后通过实例阐述了算法的有效性.     

Extraction of the Euclidean skeleton based on a connectivity criterion

The skeleton is essential for general shape representation. The commonly required properties of a skeletonization algorithm are that the extracted skeleton should be accurate; robust to noise, position and rotation; able to reconstruct the original object; and able to produce a connected skeleton in order to preserve its topological and hierarchical properties. However, the use of a discrete image presents a lot of problems that may influence the extraction of the skeleton. Moreover, most of the methods are memory-intensive and computationally intensive, and require a complex data structure.In this paper, we propose a fast, efficient and accurate skeletonization method for the extraction of a well-connected Euclidean skeleton based on a signed sequential Euclidean distance map. A connectivity criterion is proposed, which can be used to determine whether a given pixel is a skeleton point independently. The criterion is based on a set of point pairs along the object boundary, which are the nearest contour points to the pixel under consideration and its 8 neighbors. Our proposed method generates a connected Euclidean skeleton with a single pixel width without requiring a linking algorithm or iteration process. Experiments show that the runtime of our algorithm is faster than the distance transformation and is linearly proportional to the number of pixels of an image.     

基于支持向量机的彩色图像边缘检测

提出了一种新的基于支持向量机的彩色图像边缘检测算法.将彩色图像像素3×3邻域内像素的RGB值表示为一个27维的向量,作为该像素的特征,利用支持向量机直接判断其是否为边缘点.针对实际图像的边缘检测实验表明,支持向量机可以有效地进行彩色图像的边缘检测,其检测效果可以和传统的Sobel等边缘检测算子相当.     

基于最大目标函数的医学图像边缘检测

鉴于医学图像特点和传统算法的缺点,提出一种新的医学图像边缘检测算法,该算法通过考察3×3模板的理想边缘结构特征,将模板像素沿边缘方向分为两个集合,通过距离度量函数构造适当的目标函数,计算四个方向的目标函数值,从而获得最大目标函数值,并与给定的阈值比较,应用非极大抑制方法,判别该象素是否为边缘点,最后进行双阈值连接,可得到单像素边缘图像。仿真结果表明,该算法能够有效检测医学图像边缘,所检测出的图像边缘细节丰富,单像素宽,定位准确。     

A spatial–spectral kernel-based approach for the classification of remote-sensing images

Classification of remotely sensed images with very high spatial resolution is investigated. The proposed method deals with the joint use of the spatial and the spectral information provided by the remote-sensing images. A definition of an adaptive neighborhood system is considered. Based on morphological area filtering, the spatial information associated with each pixel is modeled as the set of connected pixels with an identical gray value (flat zone) to which the pixel belongs: The pixel's neighborhood is characterized by the vector median value of the corresponding flat zone. The spectral information is the original pixel's value, be it a scalar or a vector value. Using kernel methods, the spatial and spectral information are jointly used for the classification through a support vector machine formulation. Experiments on hyperspectral and panchromatic images are presented and show a significant increase in classification accuracies for peri-urban area: For instance, with the first data set, the overall accuracy is increased from 80% with a conventional support vectors machines classifier to 86% with the proposed approach. Comparisons with other contextual methods show that the method is competitive.     

自适应中心加权的彩色图像中值滤波方法

提出一种彩色图像自适应中心加权的矢量中值滤波方法。通过引入投影距离确定与待滤波点最接近的若干个像素的关系,并考虑该点与由聚合距离所确定的中值的关系,自适应地确定该点的权系数,形成相应模板下的一个多重向量集合,将该集合的中值作为滤波中值。实验证明所提出方法能有效地去除彩色图像的椒盐噪声,同时能够较好地保持其色调和细节信息。     

Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system

A novel and accurate method for matching of heterogeneous faces, such as sketch and near-infrared (NIR) images, with the visible (VIS) photo gallery and vice a versa has been presented here. A new geometric edge-texture feature (GETF) is proposed, which is not only able to capture the edge information but also the texture information. GETF is constructed from the combined information of edge and texture image of same individual. For texture information local binary pattern (LBP) is used, while for edge information canny edge detection is chosen. Edges are sensitive to illumination, so before applying canny edge operation, we convert the image into illumination invariant gradient domain. For each pixel of the edge image, the nearest edge pixel is found. Finally, the total hamming distance between any pixel and its nearest edge pixel of the corresponding texture image gives GETF_Dist and the angle between them gives the GETF_Ang feature. To classify the heterogeneous faces we proposed a multiple fuzzy-classifier system, which is a combination of fuzzy partial least square (FPLS) and fuzzy local feature-based discriminant analysis (FLFDA). We have tested statistically that, the proposed classifier performs better than the individual classifiers. In sketch-photo matching, a rank-1 accuracy of 99.66% is achieved in a gallery of 606 photos consisting of CUHK student dataset, AR face dataset, and XM2VTS dataset. In NIR–VIS image matching, a rank-1 accuracy of 99.50% is achieved in a gallery of 400 VIS images from CASIA-HFB dataset.     

Skeleton growing: an algorithm to extract a curve skeleton from a pseudonormal vector field

A curve skeleton is used to represent a 3D object in many different applications. It is a 1D curve that captures topology of the 3D object. The proposed method extracts a curve skeleton from the vector field inside the 3D object. A vector at each voxel of the 3D object is calculated using a pseudonormal vector. By using such a calculation, the computation time is significantly reduced compared with using a typical potential field. A curve skeleton is then extracted from the pseudonormal vector field by using a skeleton-growing algorithm. The proposed algorithm uses high-curvature boundary voxels to search for a set of critical points and skeleton branches near high-curvature areas. The set of detected critical points is then used to grow a curve skeleton in the next step. All parameters of our algorithms are calculated from the 3D object itself, without user intervention. The effectiveness of our method is demonstrated in our experiments.     

基于支持向量机的多特征彩色图像边缘检测

针对在特殊领域中彩色图像边缘检测,不仅需要准确地检测到目标边缘而且需要去除非目标边缘,提出了一种新的支持向量机多特征彩色图像边缘检测方法.这种方法根据彩色图像边缘的特点,在图像亮度和色度通道上结合像素加权梯度值和像素邻域相关信息构建多维特征向量,通过训练的支持向量机可以准确识别出目标边缘.实验结果表明,该方法比传统边缘检测方法具有更好目标边缘识别能力.     

一种基于边缘检测的局部阈值分割算法

本文提出了一种基于边缘检测的局部阈值分割方法.该方法将整幅灰度图像分成小块,在每个小块中利用梯度算子对小块中的边界点进行检测,寻找出小块内的所有边界点;然后沿着这些边界点的梯度方向找出最临近的点,以所有这些临近点和边界点的灰度均值作为该小块的灰度阈值进行分割.该算法计算复杂度较低,避开了灰度直方图阚值分割方法中“谷底”难以确定的问题,同时照顾到了图像的局部灰度特性.     

An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.