基于边缘信息的图像阈值化分割方法

针对现有几种利用边缘信息来进行图像闽值分割的方法存在的对噪声高度敏感，梯度阈值难以选取，且不具有自适应性的特点，提出一种抗噪声影响的形态学梯度算子和一种基于梯度直方图统计特征的梯度闽值自适应选取算法，得出了一套完整的基于边缘信息的图像闽值化分割算法。     

具有二值倾向性的阈值分割算法应用研究

对具有二值倾向性的图像,较好的算法是闽值分割,阈值化分割算法主要有两个步骤：确定需要的分割闲值;将分割阚值与像素值比较来划分区域。本文以具有二值倾向性某雕塑图像为例,给出了改进的阈值分割算法,在理论上证明了其误差的极小性,并给出了关键的操作技巧。     

基于粗糙集理论的图像分割智能决策方法

尽管如今已有多种图像分割算法,但是没有任何一种分割方法能够适用于所有的图像.为了使图像跟踪系统能根据图像特征自适应选取分割算法,给出了一种基于粗糙集理论的图像分割智能决策方法.该方法首先选取若干具代表性的分割算法构成算法库,并用它们对各种样本图像进行分割;然后利用从样本图像中提取出来的各种数值特征,并根据图像分割质量评价标准评判出各样本图像的最优分割算法,用其构成决策信息表;最后应用粗糙集理论来对决策信息表进行离散化处理和属性约简,以生成图像分割算法选取的决策规则.该决策方法解决了图像跟踪系统中分割算法选取的一系列难题.实验证明,该决策方法能比较有效地根据系统所处理图像的特征选取出算法库中最优的分割算法,并可满足车载图像跟踪系统的实时性要求.     

红外图像分割算法分析

根据红外图像的特点,提出了一种测量目标红外辐射面积的算法。采用二维otsu算法和变闽值统计平均算法的目标图像进行分割算法,采用均值算法和中值算法去除图像噪声,采用二维otsu算法初二值化去噪后图像,在以此阂值为基准变换闽值得到一组二值化后图像,统计平均二值化后的图像,设定阀值得到最终二值化结果,在结合原图像得到分割结果。最后通过与一维和二维otsu图像分割算法试验比较,验证了此算法的有效性。     

基于口腔图像的分割算法的研究

针对传统分割算法难以解决多目标分割等问题，提出了一种改进的一维Kaput熵多闽值分割算法。该算法依据Kaput熵阈值选择原理，应用图像灰度直方图信息，利用迭代合并和选择方法建立口腔图像中的阈值分割模型，解决了图像分割中阂值的自动获取问题和多阈值并行选择问题，实现了口腔图像中牙齿和病灶的分离。形状准则和一致性准则评价方法证明了该算法在抗噪声方面明显优于自适应阈值方法。获得的分割结果较好地保留了图像的灰度信息和边缘信息，为后续的图像分析和诊断工作提供了保证。     

基于小波一形态学的车牌图像分割算法

车辆牌照图像的分割是车牌识别的前提和基础。在车辆自动管理技术日益发展的今天,车牌图像的分割技术倍受瞩目。针对牌照与车身背景的分割问题,提出了基于小波变换和数学形态学方法的车辆牌照阈值分割算法。首先,对车牌图像进行小波去噪,然后利用数学形态学对去噪后的车牌图像进行闽值分割。此方法能够从含有较强噪声的车辆图像中获取车牌图像,通过构造适当的结构元素,可以达到比较理想的提取效果。     

浅论遥感图像中特定目标的自动识别

本文介绍了一个TM遥感图像中城市居民地的自动识别系统。通过分析了TM影像的特点．选择最佳的各波段组合方案进行波段合成,接着利用闽值分割算法对合成的假彩色位图进行分割,然后采用面积分析算法对图像进行去噪处理,再对图像进行聚类处理来提取特定目标,最终识别出特定目标。     

图象分割中阈值选取方法的研究及其算法实现

在数字图，象处理中，图像分割是其中的一个重要问题，也是一个经典难题。基于阈值选取方法的图象分割，计算简单，具有较高的运算效率；其方法多种多样，但是方法选取直接影响到图象分割的质量，本文针对常见阈值选取方法的具体使用情况进行了比较研究，并提出了具体算法实现。以便对从事图象处理的科研人员提供一定的借鉴作用。     

一种光斑图像的阈值分割和光斑中心坐标的计算方法

工业测量中经常需要从获取的光斑图像中提取光斑区域．计算光斑中心的坐标。针对光斑图像具有背景区域较暗且面积大,目标（光斑）区域较亮且面积小的特点,提出了一种适用于光斑图像的阈值分割方法．计算单个光斑中心坐标的重心法．以及采用连通区域标记和区域大小排序计算多个光斑中心坐标的方法。与其他闽值分割方法比较后的实验结果表明,所提出的方法可更好地分割光斑图像。与期望的人工选取的闽值最接近．计算得到的光斑中心坐标准确．且运行时间较短。     

基于微粒群算法的OTSU图像分割方法

本文研究了图像闽值分割方法中常用的OTSU算法,在此基础上提出了基于微粒群算法的OTSU图像分割方法,该方法利用微粒群算法的快速全局寻优,确定图像的分割闯值所对应的灰度值,在该灰度值下目标和背景的方差最大。实验中选用了像素分别是8位和16位的两幅图片来进行分割,得到了较好的分割效果,而且该方法大大提高了计算速度,是一种有效的图像分割方法。     

一种面积的近似算法

为了解决在计算机中对由众多线条构成的封闭图形要计算其面积单靠简单地分割图形已不行的问题 ,能准确快速地获得封闭图形的面积 ,针对其问题提出了一种用计算机自动计算封闭图形面积的方法     

面向隶属度修正模糊聚类的参数选择方法

隶属度修正是模糊C-均值聚类算法改进的一个重要方向,该类改进算法引入模糊阈值修正隶属度,极大的加快了算法的收敛.然而其模糊阈值的自适应取值一直是一个较难解决的问题.针对这个问题,从数据对聚类中心的物理吸引和相似关系等角度提出了一种针对隶属度修正类FCM算法的模糊阈值参数选择方法,并从该参数选择公式的单调性、收敛性和鲁棒性等角度理论验证了该方法的有效性.仿真实验表明,该参数选择方法有效并具有较好的自适应效果,在加入离群点时也有着较强的鲁棒性,对于隶属度修正类FCM算法的参数选择有着较高的应用价值.     

GENETIC ALGORITHM APPROACH TO A LUMBER CUTTING OPTIMIZATION PROBLEM

Genetic algorithms are a technique for search and optimization based on the Darwinian principle of natural selection. They are iterative search procedures that maintain a population of candidate solutions. The best or most fit solutions in that population are then used as the basis for the next generation of solutions. The next generation is formed using the genetic operators reproduction, crossover, and mutation. Genetic algorithms have been successfully applied to engineering search and optimization problems. This paper presents a discussion of the basic theory of genetic algorithms and presents a genetic algorithm solution of a lumber cutting optimization problem. Dimensional lumber is assigned a grade that represents its physical properties. A grade is assigned to every board segment of a specific length. The board is then cut in various locations in order to maximize its value, A genetic algorithm was used to determine the cutting patterns that would maximize the board value.     

Evaluation of algorithms for one-dimensional cutting

The paper deals with the problem of evaluating and comparing different one-dimensional stock cutting algorithms regarding trim loss. Different types of problems are identified. An evaluation method is developed which enables a comparison of solutions of all types of problems. A practical example of this methods implementation is presented.Scope and purposeThere are many algorithms and methods for one-dimensional stock cutting with different factors that need to be taken into account. Therefore a general comparison between them is very difficult if not impossible. However, if we assume that trim loss is the most important factor common to different methods, we can overcome this problem by limiting the comparison to trim loss. In different cutting stock problems and in different approaches to them trim loss is defined differently. For the comparison of different solutions to be possible, we need to find a common definition to the trim loss. Such a general definition is introduced by the General One-Dimensional Cutting Stock Problem type (G1D-CSP). In this paper, a problem generator algorithm PGEN for G1D-CSP is presented and the method for evaluation and comparison of different one-dimensional cutting stock algorithms is proposed.     

一种改进的车牌图像二值化方法

讨论了图像阈值分割方法,提出了改进的Bernsen图像分割方法,并用VC 进行了仿真,该方法能够有效实现文本图像的分割,可以有效避免字符的伪影以及断裂,适合汽车牌照图像二值化。     

Sequential value correction heuristic for the two-dimensional cutting stock problem with three-staged homogenous patterns

A sequential value correction heuristic is presented for the two-dimensional cutting stock problem with three-staged homogenous patterns, considering both input-minimization and simplicity of the cutting process. The heuristic constructs many cutting plans iteratively and selects the best one as the solution. The patterns in each cutting plan are generated sequentially using simple recursive techniques. The values of the item types are corrected after the generation of each pattern to diversify the cutting plans. Computational results indicate that the proposed heuristic is more effective in input minimization than published algorithms and commercial stock cutting software packages that use three-staged general or exact patterns.     

矩形件简单块占角排样方式的动态规划

目的 针对矩形件无约束2维剪切排样问题,提出一种可简化板材切割工艺的简单块占角排样方式,并构造这种排样方式的动态规划生成算法。方法 该排样方式在板材左下角按照简单块方式排样若干行若干列同种矩形件,将板材剩余部分划分为两个子板;将子板按照上述方法继续递归排样和划分,直至子板排满矩形件为止。采用动态规划确定所有可能尺寸的板材左下角排样的最优矩形件、矩形件的最优行列数和板材剩余部分的最优子板划分。运用规范尺寸排除不必要的计算。结果 将本文算法与目前常见的算法进行比较,实验结果表明本文算法计算时间合理,排样价值较高。在第1组41道基准例题中,本文算法所有例题均求出了精确解,同质块T型算法、同质块两段算法和复合条带两段算法分别有7道、5道和4道例题未求出精确解。在第2组20道基准例题中,本文算法只有1道例题未求出精确解,普通三阶段算法、同质块T型算法、同质块两段算法和匀质条带三块算法分别有18道、15道、15道和20道例题未求出精确解。在第3组50道随机例题中,本文算法、普通两段算法和同质块两段算法板材利用率分别为99.913 7%、99.862 3%和99.796 1%。在第4组31道基准例题中,本文算法所有例题均求出了精确解,普通占角排样算法有2道例题未求出精确解。结论 本文算法计算时间远小于精确算法,优化效果接近精确算法;本文算法计算时间与多种启发式算法接近,但优化效果好于多种启发式算法。     

An Efficient Algorithm for Solving Pseudo Clique Enumeration Problem

The problem of finding dense structures in a given graph is quite basic in informatics including data mining and data engineering. Clique is a popular model to represent dense structures, and widely used because of its simplicity and ease in handling. Pseudo cliques are natural extension of cliques which are subgraphs obtained by removing small number of edges from cliques. We here define a pseudo clique by a subgraph such that the ratio of the number of its edges compared to that of the clique with the same number of vertices is no less than a given threshold value. In this paper, we address the problem of enumerating all pseudo cliques for a given graph and a threshold value. We first show that it seems to be difficult to obtain polynomial time algorithms using straightforward divide and conquer approaches. Then, we propose a polynomial time, polynomial delay in precise, algorithm based on reverse search. The time complexity for each pseudo clique is O(Δlog |V|+min {Δ ²,|V|+|E|}). Computational experiments show the efficiency of our algorithm for both randomly generated graphs and practical graphs.     

Adaptive aggregation methods for infinite horizon dynamicprogramming

A class of iterative aggregation algorithms for solving infinite horizon dynamic programming problems is proposed. The idea is to interject aggregation iterations in the course of the usual successive approximation method. An important feature that sets this method apart from earlier ones is that the aggregate groups of states change adaptively from one aggregation iteration to the next, depending on the progress of the computation. This allows acceleration of convergence in difficult problems involving multiple-ergodic classes for which methods using fixed groups of aggregate states are ineffective. No knowledge of special problem structure is utilized by the algorithms     

Analysis of distributed genetic algorithms for solving cutting problems

In this paper, a solution to the three‐stage two‐dimensional cutting problem is presented by using sequential and parallel genetic algorithms (GAs). More specifically, an analysis of including distributed population ideas and parallelism in the basic GA are carried out to solve the problem more accurately and efficiently than with ordinary sequential techniques. Publicly available test problems have been used to illustrate the computational performance of the resulting metaheuristics. Experimental evidence in this work will show that the proposed algorithms outperform their sequential counterparts in time (high speedup with multiprocessors) and numerically (lower number of visited points during the search to find the solutions).