基于遗传算法的SAR图像自动道路提取

为了有效地进行SAR图像道路目标自动提取,提出了一种基于遗传算法的SAR图像道路目标自动提取方法。该方法首先通过Frost滤波器去相干斑;然后利用乘性Duda线特征检测算子进行线特征检测,接着利用Radon变换进行线基元提取,再利用遗传算法进行线基元连接;最后利用蛇模型调整道路位置并进行道路鉴别。在星载和机载SAR图像上进行的实验以及性能定量评估结果证明了该方法的有效性。     

基于遗传算法的SAR图像道路网检测方法

提出了一种基于遗传算法的SAR图像道路网检测算法.该算法以道路在SAR图像中呈黑色直线状结构为基本出发点,首先检测线特征点以获取潜在道路点;接着利用基于每个连通区域上的Radon变换提取线基元;然后从图像上最长的线基元出发,以其为种子基元,在其周围确定一个搜索区域,用遗传算法选择与种子基元共线的线基元进行连接,并更新种子基元,直到完成所有的连接,得到候选道路段.为了使检测道路更准确,利用蛇模型调整道路段的位置,然后用道路的特征进行鉴别.最后检测道路的交叉点,完成整个道路网的检测.机载SAR图像的实验结果及定量分析均证明了该算法的有效性.     

用遗传算法提取基元

基元提取是基于模型的计算机视觉的一项重要任务.Hough变换是基元提取的最常用的方法,然而,在许多情况下,它的存贮开销太大而难以让人接受.近些年来,有些人用统计学方法来提取基元,但如何构造合适的代价函数仍是一个困难问题.基元提取等同于寻找具有多个局部极小值的代价函数的最优解.遗传算法(Genetic algorithms)能够有效地在搜索空间中找出全局最优解.为实现有效的基元提取,作者从几何数据点中随机地选择一组最小子集,然后用遗传算法对几何数据点进行动态划分,经过若干次进化将得到一个最优划分,与之对应的基元和基元所对应的数据点将被提取出来.这种算法可用于多种基元和多个基元的提取.     

基于内唇轮廓标定的唇印提取算法

针对现有特征唇印提取中信息处理量较大、识别率不高等问题,本文提出了基于内唇轮廓的特征唇印提取算法。算法首先在基元图像上对内唇轮廓特征点进行标定,建立基元唇印模型,然后通过均值计算构造普通模型,并利用Gabor变换对基元唇印和普通模型进行联合特征信息提取,最后通过相似度对比选择出特征唇印,以实现身份识别。仿真实验验证,本文的特征唇印提取算法在较低时空消耗下,具有较高的识别率,具有有效性和可用性。     

基于多元图结构子模式表示的模式识别方法*

针对统计模式识别和结构模式识别方法各自的局限性,提出一种基于多维数据多元图结构子模式表示（包括图形基元和特征基元表示）的模式识别方法,它体现了模式识别问题研究的一种新思维,具有鲜明的可视化特点。应用多维数据多元图表示原理实现无结构数据的结构化表示,提取出表征多元图图形的图形基元和特征基元,将对象表达为多元图结构特征子模式进行分类识别。利用UCI机器学习数据库中的Iris数据进行了分类实验,实验对比结果显示该方法具有较好的识别效果。     

高分辨率遥感影像信息提取及块状基元特征提取

分析了基于目标基元的遥感信息提取技术相对于传统的基于像元的处理方式所具有的优势;结合人类视觉机制和计算机尺度空间理论,设计了基于特征基元的高分辨率遥感影像多尺度信息提取技术流程框架,即在大尺度下进行粗分割后进行块状基元提取的技术及实现过程;最后给出了相关实验结果,实验表明本文提出的基于基元的遥感信息提取便于与视觉知识和环境知识更好地结合,提高了遥感信息处理与应用的自动化和智能化水平。     

遥感图像中水上桥梁的识别

本文利用知识基对水上桥梁识别问题进行了讨论,提出了一种适应性更强的识别方法.该方法建立在人工智能语言V.3平台上采用了模型驱动,将识别过程分为低中高三层处理过程,低层处理得到团块基元,中层处理从团块基元中提取特征,高层处理进行模型匹配,同时给出了两个实验例子.     

基于先验形状的人工目标识别与轮廓恢复

以具有典型形状特征的操场为例,探讨了形状特征在人工目标自动识别中的应用。基于形状特征的人工目标识别,一般首先将图像分割为多个基元,然后依据人工目标有较规则形状的特点,以形状特征作为指标进行识别。由于高分辨率遥感图像细节信息丰富,这种方法所提取的目标轮廓往往不完整。本文提出了"图像-基元-目标-轮廓恢复"的识别模式,在目标识别后采用加入形状先验知识的主动轮廓模型对目标进行轮廓恢复。实验结果表明,这种方法可以有效地修正目标提取结果中的轮廓缺失。     

基于免疫计算的概念提取方法研究

基于实例的模式识别中,由于存在大量实例和特征个数可变特性,导致分类器识别性能低下,难于形成具有高度概括性的共性对象实例.基于免疫计算的概念提取是在有效降低特征个数的同时,提取各类的中心,以此为实例模式对待识别样本进行分类决策.实验结果表明算法在保持甚至提高分类精度的同时,不仅有效地降低了特征个数,而且提取的类中心分类效果更好.与基于遗传算法的概念提取结果相比较,在有限代数内,该算法能收敛到更优的类中心,从而验证了算法的有效性及其应用潜力.     

基于模板匹配和神经网络的乐谱图象符头基元检测与识别

符头基元的检测与识别是光学乐谱识别技术的核心与关键之一.本文提出一种改进的模板匹配结合神经网络仲裁的方法,对乐谱图象中的符头基元进行检测和识别.在模板匹配阶段,利用乐谱的先验知识对符头的搜索范围进行有效限定,减少了匹配过程中搜索的"盲目性".匹配完成后,提取图象中的垂直直线,对相互连接的候选符头和垂直直线进行编码,通过BP神经网对编码样本的训练,实现音符构成规则的智能学习,最后由神经网络完成符头基元的仲裁确认.实验数据表明,与原有的模板匹配方法比较,该方法的整体识别性能体现出了明显的优势,具有较高的实用价值.     

Geometric primitive extraction using a genetic algorithm

Extracting geometric primitives from geometric sensor data is an important problem in model-based vision. A minimal subset is the smallest number of points necessary to define a unique instance of a geometric primitive. A genetic algorithm based on a minimal subset representation is used to perform primitive extraction. It is shown that the genetic approach is an improvement over random search and is capable of extracting more complex primitives than the Hough transform     

Volume-preserving free-form solids

Some important trends in geometric modeling are the reliance on solid models rather than surface-based models and the enhancement of the expressive power of models, by using free-form objects in addition to the usual geometric primitives and by incorporating physical principles. An additional trend is the emphasis on interactive performance. In this paper, we integrate all of these requirements into a single geometric primitive by endowing the tri-variate tensor-product free-form solid with several important physical properties, including volume and internal deformation energy. Volume preservation is of benefit in several application areas of geometric modeling, including computer animation, industrial design and mechanical engineering. However, previous physics-based methods, which have usually used some form of “energy”, have neglected the issue of volume (or area) preservation. We present a novel method for modeling an object composed of several tensor-product solids while preserving the desired volume of each primitive and ensuring high-order continuity constraints between the primitives. The method utilizes the Uzawa algorithm for non-linear optimization, with objective functions based on deformation energy or least squares. We show how the algorithm can be used in an interactive environment by relaxing exactness requirements while the user interactively manipulates free-form solid primitives. On current workstations, the algorithm runs in real-time for tri-quadratic volumes and close to real-time for tri-cubic volumes     

用VC语言实现任意多边形的Delaunay完全三角剖分算法

多边形三角剖分是计算几何的一个几何基元,它可以简化问题规模,在计算机图形学、模式识别等方面有重要的应用。本文针对已有的Ddaunay三角剖分算法的不足,提出新算法,并采用Visual C语言MFC类进行链表的管理,使得编程容易实现。整个算法简洁通用。最后给出了在实际中的应用。     

高度场八叉树的体特征表达算法

体特征表达对用户理解和认知虚拟环境有着至关重要的作用。当前的体特征表达算法由于存储量大且不易于在GPU中加速等问题,渲染效率低下,难以满足场景可视化的实时性需求。针对这一问题,提出了一种高效的高度场八叉树体特征表达算法,不仅解决了传统高度场仅能表达2.5维模型,无法表达真三维模型的问题,而且为体特征表达提供了一种新的可行途径。算法使用八叉树结构生成三维模型的高度场表示,将传统的z向高度场扩展到x,y,z三个方向的高度场。首先,提出了三角面片预处理方法,保证模型精度和数据的完整性;其次,提出了基于投影变换的高度场表示判断及栅格化方法,将几何图元转换成二维空间的高度场数据;最后,提出了基于高度场八叉树的光线投射算法。实验结果表明,算法能极大地减少存储量,具有较高的光线投射效率,表达三维模型时取得较好效果。     

基于图像空间的复杂模型碰撞检测算法

提出一种使用图形硬件用于复杂模型间的快速的碰撞检测算法.算法是基于CULLIDE的执行GPU可见性查询来减少物体模型间没有邻近特征的子集,描述了一个分类方案计算物体潜在碰撞集和碰撞自由子集,提高了裁减的性能.为了减少CPU的负载,利用GPU的可编程性,在GPU上进行精确的物体相交计算.     

Classified road detection from satellite images based on perceptual organization

Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub‐processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob‐like primitive and line‐like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre‐main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability.     

Constrained Structure and Motion From Multiple Uncalibrated Views of a Piecewise Planar Scene

This paper is about multi-view modeling of a rigid scene. We merge the traditional approaches of reconstructing image-extractable features and of modeling via user-provided geometry. We use features to obtain a first guess for structure and motion, fit geometric primitives, correct the structure so that reconstructed features lie exactly on geometric primitives and optimize both structure and motion in a bundle adjustment manner while enforcing the underlying constraints. We specialize this general scheme to the point features and the plane geometric primitives. The underlying geometric relationships are described by multi-coplanarity constraints. We propose a minimal parameterization of the structure enforcing these constraints and use it to devise the corresponding maximum likelihood estimator. The recovered primitives are then textured from the input images. The result is an accurate and photorealistic model.Experimental results using simulated data confirm that the accuracy of the model using the constrained methods is of clearly superior quality compared to that of traditional methods and that our approach performs better than existing ones, for various scene configurations. In addition, we observe that the method still performs better in a number of configurations when the observed surfaces are not exactly planar. We also validate our method using real images.