综合几何信息的实时线绘制算法

以三维网格模型的微分几何信息为依据,结合视点相关和视点无关的线绘制方法,提出一种基于GPU的实时绘制算法.基于视点曲率在图像空间中计算提取视点相关特征线,同时利用风格化纹理和主曲率信息绘制视点无关特征线.根据三维模型信息与预设计的风格化纹理,在像素着色器中对视点相关和视点无关的2类特征信息进行计算,然后结合两者结果得到令人满意的绘制结果.实验结果表明,由于所有计算在图像空间由GPU并行完成,可以高效地提取特征线;采用风格化纹理的设计增加了图像空间风格化绘制的可控性,弥补了图像空间算法的风格化可控性差的缺点.此外,文中算法不仅可以进行实时的风格化线绘制,以该算法为基础还可以进行进一步的艺术仿真,如国画的模拟等.     

基于视觉曲率估算的文物线图绘制方法

针对表面富含噪声的文物三维模型特征线的有效提取问题,分析了预处理阶段导致纹理细节丢失的原因,提出了一种基于视觉曲率估算的文物线图自动绘制方法。首先,对三角网格模型顶点的高度函数空间进行均匀采样,通过统计高度函数中极值点的个数,实现对模型顶点的视觉曲率估算;然后,根据多尺度约束下模型顶点的视觉曲率分布,将模型划分为平坦区域和特征区域;接着,依据考古领域均值对特征区域的顶点进行锐化滤波,计算出新的三角网格模型的顶点坐标;最后,对三角网格模型提取特征轮廓线,实现对文物线图的自动绘制。实验结果表明,基于视觉曲率的特征轮廓线在保留文物模型表面纹理细节的同时,有效避免了简单脊线/谷线法绘制线图呈现的尖锐现象。     

大地形空间格网点平均曲率算法研究及可视化

研究地形绘制问题,为了实现在计算机中实时可视化,在大地形的场景绘制时需要在高曲率处保留尽量多的矢量数据(点、线、面),在低曲率处删除尽量多的矢量数据.采用了在细节层次模型简化技术中,空间格网点的平均曲率的计算方法,以及在.NET平台下用C#语言对算法进行编程.经计算得出格网点曲率值的大小,删除低曲率的中心点并对删除中心点后留下的空洞进行三角化,从而实现细节层次模型的简化,大大地减少了大地形场景中矢量数据(点、线、面)的数量.仿真试验结论表明,提出的平均曲率算法能较好地解决矢量数据量大与计算机实时处理能力有限的矛盾,满足了大地形中大规模矢量数据三维可视化的实时绘制需求.     

Snake模型与深度凹陷区域的分割

在基于Snake模型的图像分割中,深度凹陷区域的分割是一个难点．尽管GVFSnake模型极大地改善了这个问题,但它需要事先求解一个偏微分方程组,增大了计算量;同时,GVFsnake模型在初始化时还存在一个“临界点”问题．探讨了深度凹陷区域的分割,用离散轮廓上顺序3点所成三角形内切圆圆心来定义离散轮廓的曲率,该曲率既能反映轮廓的弯曲程度,又具有合理的方向．基于该曲率定义了曲率外力项,构造了基于Snake模型的两阶段算法,先采用传统Snake模型使离散轮廓逼近目标边缘,然后在曲率外力的作用下使离散轮廓进入目标的凹陷区．曲率外力项的引入能较好地解决深度凹陷区域的分割问题,也可以扩展该外力项来扩大Snake模型的捕捉范围．实验结果表明该方法是有效的．     

全局各向异性四边形主导网格重建方法?

四边形网格的结构特点要求网格单元满足全局一致性,难以取得网格质量与表达效率之间的平衡。为此,提出一种基于全局的各向异性四边形主导网格重建方法,可生成网格质量好且冗余程度低的四边形网格。重建过程以主曲率线为基本采样单元,首先计算模型表面的主曲率场并对主曲率场积分,得到密集的主曲率线采样;再根据贪心算法,利用几何形体自身的各向异性找出冗余度最高的主曲率线并予以删除;如此循环,直至达到理想的采样密度。该重建方法适用于任意拓扑网格模型,所得到的各向异性四边形主导网格在网格模型分辨率下降时,由于始终保留重要主曲率线,从而可以更好地保持模型特征。同时,在基于贪心算法的渐进式主曲率线删除过程中,可产生分辨率连续可调的四边形主导网格。     

图像修复的CDD模型新算法

CDD图像修复模型是在TV模型的基础上引入曲率项来满足视觉的“连接性”原则. 考虑到该模型各参考点梯度模值大小的不同及扩散效率的不足,本文结合退化扩散模型提出了一种新的CDD模型图像修复算法. 该算法利用待修复区域像素点的梯度和等照度线的曲率信息来控制各参考点对破损区域的贡献值,并进一步作加权处理. 最后将该模型的偏微分方程离散化,给出了模型的数值实现方法. 实验结果表明该算法能保证图像边缘的视觉连通性,并在相同迭代次数的情况下,修复效果明显优于CDD算法,是一种有效的新方法.     

三角形网格模型顶点曲率的求解算法*

推导了一般三角形网格模型顶点的平均曲率、高斯曲率和主曲率的计算方法,考虑到经常遇到粗糙三角形网格模型,为提高其曲率计算方法的精度,结合Loop细分曲面算法,进一步拓展了该曲率计算方法.该算法用于具有特征保持的网格模型简化取得了良好的效果.     

全局各向异性四边形主导网格重建方法

四边形网格的结构特点要求网格单元满足全局一致性,难以取得网格质量与表达效率之间的平衡.为此,提出一种基于全局的各向异性四边形主导网格重建方法,可生成网格质量好且冗余程度低的四边形网格.重建过程以主曲率线为基本采样单元,首先计算模型表面的主曲率场并对主曲率场积分,得到密集的主曲率线采样;再根据贪心算法,利用几何形体自身的各向异性找出冗余度最高的主曲率线并予以删除;如此循环,直至达到理想的采样密度.该重建方法适用于任意拓扑网格模型,所得到的各向异性四边形主导网格在网格模型分辨率下降时,由于始终保留重要主曲率线,从而可以更好地保持模型特征.同时,在基于贪心算法的渐进式主曲率线删除过程中,可产生分辨率连续可调的四边形主导网格.     

采用高斯差分算子的拉普拉斯线绘制

近年来,图像空间的线绘制技术在绘制质量和算法鲁棒性方面取得了长足的进步.但是,图像空间的线绘制算法不能生成特征线的空间几何信息,因而应用面不如物体空间的线绘制技术广泛.针对现有物体空间线绘制算法的绘制效果和绘制速度还远不如图像空间算法,提出一种物体空间线绘制算法.在预处理阶段,采用网格模型的高斯差分算子计算法向拉普拉斯;在实时绘制时,通过计算法向拉普拉斯与视线方向的点积,从而快速计算特征线的三维几何.在该算法框架下,进一步提出各向异性的高斯差分算子,在计算法向拉普拉斯时对于特征线的梯度方向和切线方向做不同的滤波处理,从而更好地反映特征线的走向.实验结果表明,文中算法比现有的其他物体空间线绘制算法更加鲁棒,对于包含几何噪声和非均匀网格化的三维模型,其生成的线绘制结果更加光顺,能更清晰地揭示模型的形状特征;在实时绘制效率方面,能实时生成包含上百万三角面片的网格模型的线绘制结果.     

基于GPU的实时线绘制及水墨仿真研究

为了由简单三维输入自动生成水墨仿真风格图像,提出了一种基于GPU的实时线绘制算法及水墨仿真算法。提出的微分几何线绘制算法结合了视点相关和视点无关两类特征线的绘制,利用了多种曲率信息和风格化纹理。由于GPU加速和风格化纹理的设计,该算法达到实时且风格化可控,方便了国画仿真,仿真计算将水墨看作二维流体,利用LB方法模拟水墨在宣纸中的边缘扩散。通过对比大量的绘制结果以及和其他方法的比较,结果表明,该方法的绘制结果更加清晰逼真,绘制实时完成且完全自动化。     

一种改进的汉字骨架提取算法

为了更有效地提取汉字的骨架,提出一种基于内切圆提取汉字骨架的新方法。该方法首先对Windows系统中的汉字进行边缘检测,提取出汉字的边缘,然后迭代计算特定步长范围内的内切圆,将各内切圆的圆心连接起来得到书法汉字的骨架。实验结果表明,该方法在提取书法汉字骨架的同时保留了原始汉字的边缘信息,提取的骨架具有良好的对称性和连通性。     

Analysis of thinning algorithms using mathematical morphology

A precise definition of digital skeletons and a mathematical framework for the analysis of a class of thinning algorithms, based on morphological set transformation, are presented. A particular thinning algorithm (algorithm A) is used as an example in the analysis. Precise definitions and analyses associated with the thinning process are presented, including the proof of convergence, the condition for one-pixel-thick skeletons, and the connectedness of skeletons. In addition, a necessary and sufficient condition for the thinning process in general is derived, and an algorithm (algorithm B) based on this condition is developed. Experimental results are used to compare the two thinning algorithms, and issues involving noise immunity and skeletal bias are addressed     

Curve-skeleton extraction using iterative least squares optimization

A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object's geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies, 2) extracting curve skeletons through the thinning algorithm, and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the pre-computational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other well-known methods. Experimental results show advantages when using our method over other techniques.     

Euclidean skeletons of digital image and volume data in linear time by the integer medial axis transform

A general algorithm for computing Euclidean skeletons of 2D and 3D data sets in linear time is presented. These skeletons are defined in terms of a new concept, called the integer medial axis (IMA) transform. We prove a number of fundamental properties of the IMA skeleton, and compare these with properties of the CMD (centers of maximal disks) skeleton. Several pruning methods for IMA skeletons are introduced (constant, linear and square-root pruning) and their properties studied. The algorithm for computing the IMA skeleton is based upon the feature transform, using a modification of a linear-time algorithm for Euclidean distance transforms. The skeletonization algorithm has a time complexity which is linear in the number of input points, and can be easily parallelized. We present experimental results for several data sets, looking at skeleton quality, memory usage and computation time, both for 2D images and 3D volumes.     

Parallel Thinning Algorithms on 3D (18, 6) Binary Images

Thinning algorithms on binary images are used to generate skeletons that preserve the same connectivity structures as the objects in the original images. Two kinds of skeletons may be appropriate for 3D thinning algorithms: digital curves and digital surfaces. We propose two thinning algorithms on 3D (18, 6) binary images. One algorithm generates skeletons as digital curves and the other algorithm generates skeletons as digital surfaces. Both algorithms are 6-subiteration algorithms—in each iteration, they are applied alternatively to delete border voxels from each of the six directions, upper, lower, north, south, east, and west.     

Skeleton-enhanced line drawings for 3D models

We present a novel line drawing approach for 3D models by introducing their skeleton information into the rendering process. Based on the silhouettes of the input 3D models, we first extract feature lines in geometric regions by utilizing their curvature, torsion and view-dependent information. Then, the skeletons of the models are extracted by our newly developed skeleton extraction algorithm. After that, we draw the skeleton-guided lines from non-geometric regions through the skeleton information. These lines are combined with the feature lines to render the final line drawing result using the line optimization. Experimental results show that our algorithm can render line drawings more effectively with enhanced skeletons. The resulting artistic effects can capture the local geometries as well as the global skeletons of the input 3D models.     

基于骨架的血细胞图像分离算法

文章从细胞图像骨架的角度出发,研究和发展了一种新的粘连细胞分离算法。其主要思路是:首先采用细化算法提取细胞图像的骨架,并计算骨架各点的边界距离值,然后利用骨架边界距离函数波谷的特性将相互粘结的细胞分离。新算法提出了一种计算骨架边界距离值的新方法用于分析骨架波谷的特性,并在理论和实践的基础上,研究和发展了细胞图像中粘结细胞的分离准则。为证明该算法的有效性,选取了上百种不同的细胞图像进行了测试。实验结果表明,新算法能有效、快速地对细胞图像中相互粘结的细胞进行合理分割。     

改进的多分辨率Reeb图骨架抽取算法

针对实际应用中的模型可能包含多个连通分量的情形,改进了多分辨率Reeb图中的骨架创建算法,并对多种不连通的模型进行了实验。生成的骨架可为三维检索提供特征描述符。     

一种基于Delaunay三角化的手写体文字细化方法

为了对手写体文字进行快速准确的识别，基于Delaunay三角化方法，提出了一种新的文字图象细化算法，该算法首先通过对文字图象边界的近似多边形进行Delaunay三角化，同时把其分成一系列保持拓扑关系的三角形，然后根据三角形的类型生成不同的局部骨架；最后连接生成整文字图象的骨架，由于该算法充分利用了图象的全局和局部信息，因此具有速度快，效果好等优点。     

Empirical mode decomposition on skeletonization pruning

This paper presents a novel skeleton pruning approach based on a 2D empirical mode like decomposition (EMD-like). The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the object contour is decomposed by empirical mode like decomposition (EMD-like), the IMFs of the object provide a workspace with very good properties for obtaining the object's skeleton. The theoretical properties and the performed experiments demonstrate that the obtained skeletons match to hand-labeled skeletons provided by human subjects. Even in the presence of significant noise and shape variations, cuts and tears, the resulted skeletons have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches as many existing skeleton pruning methods and moreover, does not displace the skeleton points, which are all centers of maximal disks.