求解二值图像连通域的改进算法

介绍了一种改进的二值图像连通域快速标记方法。该算法首先找出二值图像中每行的像素直线段,接着利用链表来确定它们之间的连通关系,以此来克服同类算法中像素重复标记和标记归并需大量运算等缺陷,具有一定的使用价值。     

一种基于线的标号传播二值图象连通体快速检测方法

本文介绍一种新的基于线的二值图象连通体快速检测方法。这种方法首先对二值输入图象施行一个基于扫描象素线的标记传播过程，同一连通体不同标号子连通部分的匹配则通过对一个二值连接矩阵的行列跟踪扫描来快速实现。这种方法可在一次模式扫描过程中检测出各种复杂类型的连通体。     

基于标号回传的二值图像连通体标记算法

提出一种基于游程标号回传的二值图像连通体标记算法,该算法以游程为处理对象,将目标结构中的标号传播到游程结构中,进行游程连通性判断,将与当前游程连通的游程中最小值回传到对应的目标结构中,确保在同一连通域中有相同的根标号,进而完成二值图像标记。该算法对二值图像可以实现一次性标记,同时完成连通区域的面积、质心等特征信息的提取。具有占用内存小、实现简单、能标记任意复杂连通区域的优点,可用于红外弱小目标的检测。     

基于FPGA的快速连通区域标记算法的设计与实现

针对无行消隐图像不间断输入的高速图像处理情况,提出一种快速连通区域标记算法的硬件实现方法。利用游程编码优化标号生成算法,减小临时标号数量和等价表长度,并可同时完成特征提取;利用逐像素扫描法,以单时钟周期实现标号跟踪;利用等价表合并方法完成标号合并和特征合并。FPGA仿真结果表明：对连续输入的二值图像进行连通区域标记和特征提取时,运行时间仅由图像输入时间和等价表合并时间组成,明显优于其他方法,可适用于图像的快速识别与跟踪。     

高效的一遍扫描式连通区域标记算法

二值图像的连通区域标记算法是图像处理的一个基本问题。为了提高算法的效率,以Suzuki等人提出的多遍扫描算法为基础,提出了一种快速的一遍扫描连通域标记算法。算法通过对图像做一次正向扫描,先计算出每个当前像素所在邻域内的最小标号,再利用一个递推过程,查找该连通域中具有较小标号的结点,将被更新结点所在连通分支连接到该结点,以保证等价信息不损失。同时,用最小标号更新递推查找路径上结点的临时标号,以减小分支的深度。通过对连接表的更新使每个结点获得最终标号。算法不需要动态数据结构和递归过程的支持,需要的存储空间较小,算法比原算法速度提高了近2倍,也快于近期提出的一些基于游程的算法。     

快速连通域分析算法及其实现

本文提出一种快速连通域分析算法,它对像素的行程进行操作,并将标号作为行程及连通域的特征之一,特征通过数据结构的指针与行程及连通域相联系.该算法运用了两个关键技术,一是设计了一种链式机制来表示和实现标号的等价关系,二是通过指针的传递来实现标号及其它特征的向下传递和逆向传播,特征在标号过程中动态修改.这样甚至能实现仅对图像一遍扫描便能完成连通域标记和常用特征量的计算.实验表明了本文算法的有效性.     

基于递归的二值图像连通域像素标记算法

在研究以前二值图像连通算法的基础上，提出了一种基于递归方法的二值图像连通域像素标记算法。通过对二值图像的扫描和分析可得到二值图像中的连通域划分和连通域的数目。算法主要包括两个步骤：对输入的二值图像进行一次扫描，得到所有目标像素的连通域划分和标记的等价对表；利用递归对等价对表进行分析，得到正确的连通标记划分和连通区域数目。实验结果表明，该算法对于任意复杂形状、任意数目(小于1 000)的连通区域都能正确检测。     

基于地址-事件表示的高速二值连通域标记方法

为提高二值连通域标记的速度,将地址-事件表示AER（Address Event Representation）思想引入到二值图像处理,提出了一种基于事件对等价标号的二值连通域标记方法。该算法无需多次遍历图像中的背景点和冗余目标点,首先将待标记的连通域以AER“事件对”的方式编码保存,通过“事件对”的遍历生成临时标号和等价标记表;然后根据等价表修改临时标号;完成标号映射后最终实现连通域标记。整个算法只处理极低冗余的事件信息,避免了对全图像素的重复扫描与处理。实验结果表明,图像以AER“事件对”方式存储,数据量仅为全帧图像的10%~35%,有较高的压缩比;且该算法速度快,可达到了传统基于等价标号算法的1.5~8倍。     

一种遥感二值图像目标区域分割的新方法

基于线段扫描法进行二值图像连通域分割时,对数据量较多且形状复杂的遥感二值图像,容易使邻接表存储大量的等价对信息,即浪费存储空间也不利于算法合并处理。针对这一不足,提出了一种基于线段的快速标号算法,采用“双表”实时记录和修正等价标号,很好地解决了标记冲突的问题。经模拟数据和真实遥感二值图像验证表明,该算法比传统算法在处理效率上有显著提高,具有较好的应用价值。     

基于游程递归的连通区域标记算法

在研究已有算法的基础上,提出一种基于游程递归的标记算法,该算法可以对二值图像实现快速标记。顺序扫描图像,寻找未标记的游程,并递归搜索与之连通的游程,直到一个连通区域生成。在游程搜索过程中,在当前游程的相邻两行上,以其左端点为起始点分别向前向后进行连通游程的搜索;同时根据游程之间的位置关系对搜索策略进行优化,减少了重复搜索,提高了处理速度。该算法只需经过一次扫描图像,就能快速、准确地标记连通区域。在与已有算法的实验结果比较中,该算法具有较快的执行速度和较高的准确率,并且占用较少的内存,可以满足在施工现场中运动目标实时检测的需要。     

Fast connected-component labeling

Labeling of connected components in a binary image is one of the most fundamental operations in pattern recognition: labeling is required whenever a computer needs to recognize objects (connected components) in a binary image. This paper presents a fast two-scan algorithm for labeling of connected components in binary images. We propose an efficient procedure for assigning provisional labels to object pixels and checking label equivalence. Our algorithm is very simple in principle, easy to implement, and suitable for hardware and parallel implementation. We show the correctness of our algorithm, analyze its complexity, and compare it with other labeling algorithms. Experimental results demonstrated that our algorithm is superior to conventional labeling algorithms.     

Fast connected-component labelling in three-dimensional binary images based on iterative recursion

We propose two new methods to label connected components based on iterative recursion: one directly labels an original binary image while the other labels the boundary voxels followed by one-pass labelling of non-boundary object voxels. The novelty of the proposed methods is a fast labelling of large datasets without stack overflow and a flexible trade-off between speed and memory. For each iterative recursion: (1) the original volume is scanned in the raster order and an initially unlabelled object voxel v is selected, (2) a sub-volume with a user-defined size is formed around the selected voxel v, (3) within this sub-volume all object voxels 26-connected to v are labelled using iterations; and (4) subsequent iterative recursions are initiated from those border object voxels of the sub-volume that are 26-connected to v. Our experiments show that the time-memory trade-off is that the decrease in the execution time by one-third requires the increase in memory size by 3 orders. This trade-off is controlled by the user by changing the size of the sub-volume. Experiments on large three-dimensional brain phantom datasets (362 × 432 × 362 voxels of 56 MB (megabytes)) show that the proposed methods are three times faster on the average (with the maximum speedup of 10) than the existing iterative methods based on label equivalences with less than 1 MB memory consumption. Moreover, our algorithms are applicable to any dimensional data and are less dependant on the geometric complexity of connected components.     

Locality and Adjacency Stability Constraints for Morphological Connected Operators

This paper investigates two constraints for the connected operator class. For binary images, connected operators are those that treat grains and pores of the input in an all or nothing way, and therefore they do not introduce discontinuities. The first constraint, called connected-component (c.c.) locality, constrains the part of the input that can be used for computing the output of each grain and pore. The second, called adjacency stability, establishes an adjacency constraint between connected components of the input set and those of the output set. Among increasing operators, usual morphological filters can satisfy both requirements. On the other hand, some (non-idempotent) morphological operators such as the median cannot have the adjacency stability property. When these two requirements are applied to connected and idempotent morphological operators, we are lead to a new approach to the class of filters by reconstruction. The important case of translation invariant operators and the relationships between translation invariance and connectivity are studied in detail. Concepts are developed within the binary (or set) framework; however, conclusions apply as well to flat non-binary (gray-level) operators.     

一种二值图像连通区域标记的新方法

论文提出了一种基于区域生长的二值图像连通区域标记的快速算法。与传统方法相比,该方法的特点是在一次图像扫描中完成所有连通区域的标记,而且避免了大多数改进算法都必须处理的重复标记的问题;同时,该方法不受所标记的图形形状的影响,表现出良好的算法鲁棒性。最后分析了算法的计算复杂度,并与传统算法和两组改进算法进行了比较,试验结果表明了算法的高效率和鲁棒性。     

Data weighting method on the basis of binary encoded output to solve multi-class pattern classification problems

Data weighting is of paramount importance with respect to classification performance in pattern recognition applications. In this paper, the output labels of datasets have been encoded using binary codes (numbers) and by this way provided a novel data weighting method called binary encoded output based data weighting (BEOBDW). In the proposed data weighting method, first of all, the output labels of datasets have been encoded with binary codes and then obtained two encoded output labels. Depending to these encoded outputs, the data points in datasets have been weighted using the relationships between features of datasets and two encoded output labels. To generalize the proposed data weighting method, five datasets have been used. These datasets are chain link (2 classes), two spiral (2 classes), iris (3 classes), wine (3 classes), and dermatology (6 classes). After applied BEOBDW to five datasets, the k-NN (nearest neighbor) classifier has been used to classify the weighted datasets. A set of experiments on used real world datasets demonstrated that the proposed data weighting method is a very efficient and has robust discrimination ability in the classification of datasets. BEOBDW method could be confidently used before many classification algorithms.