Feature Point Method of Chinese Character Recognition and Its Application

A new method for recognizing Chinese characters is proposed. It is based on the so-called featurepoints of Chinese characters. The feature points we use include those on the stroke of a character, i.e., endpoints, turning points, fork points and cross points, and the key points on the background of character. Thismethod differs from the previous ones for it combines the feature points on stroke with those on back-ground and it uses feature points to recognize Chinese characters directly. A Chinese character recognitionsystem based on top-down dynamical matching of feature point is developed. The system can recognizenot only 6763 printed sample Song font Chinese characters of size 5.6×5.6mm~2 with high recognition rate,but also the general printed books, magazines and documents with a satisfactory recognition rate andspeed.     

手写体汉字笔画特征点的完整性分析

提出了一种从手写体汉字骨骼图像．上提取分叉点的改进算法。采用新的骨骼图像分析技术提取新的候选特征点集，并且证明了骨骼上所有的分叉点都可以用该特征点集中的元素表示。实验表明新特征点集中元索的数量较以前大大减少。     

Generating Chinese characters based on stroke splitting and feature extraction

There is a large demand for more fashionable style Chinese characters in advertising, art designing and publishing markets. However, it becomes challenging to create a new font style for so many Chinese characters (over 10,000). To solve this problem, a comprehensive Chinese fonts generating scheme is proposed in this paper. Firstly, a decomposition database for stroke splitting and feature extraction is proposed. Secondly, stroke segmentation rules are defined based on splitting, merging and structural model, location definition and minimum feature extraction. Thirdly, a radical searching algorithm based on stroke splitting is presented. Finally, it is realized that the generated characters can be zoomed, rotated and moved. Experimental result shows that Chinese characters with a new style can be generated rapidly with the proposed scheme. The created characters fit the real ones well with a high fidelity of 96.4%. The usability tests are run and participants’ subjective report show that the performance from the generated characters is similar to the original characters in both recognizability test and style-consistency test. The fonts generating method is also reliable for the other stroke constructed block characters such as Japanese and Korean characters.     

印刷表格文本分析识别系统的研究

本文介绍了一个印刷表格文本分析识别系统。提出了表格特征点分析方法。在表格图象处理的基础上, 对表格线进行分析, 在考虑表格线和字符块粘连的情况下提取字符块, 判别汉字串和数英串后分别识别, 生成表格。实验表明本方法的有效性。     

基于增量式迭代变换的蜡染图形渐变算法

针对蜡染图形创新, 提出一种基于增量式迭代变换法的图形渐变方法, 即在不受特征点集对应的约束下, 实现两个平面图形之间自然、平滑渐变。该方法采用迭代渐变的思想, 对图形进行矢量预处理, 并分别在源矢量图形和目标矢量图形上采样一组几何特征点, 通过等分弧长对应的规则把两个图形进行权重归中, 对采样的节点求全局最小二乘进行特征匹配与映射, 再通过增量式迭代变换法得到一系列渐变中间图形。计算机仿真结果表明, 此算法能够较好地实现非特征对应的图形渐变, 达到过渡自然、平滑的目的。     

部件轮廓汉字的自动生成

本文提出运用图形学中布尔运算的方法对笔划汉字进行自动处理而生成部件轮廓汉字，并对传统的布尔运算中的重合线段的跟踪、特殊交点的处理进行有效的改进。     

Segmentation of connected handwritten digits using Self-Organizing Maps

Segmentation is an important issue in document image processing systems as it can break a sequence of characters into its components. Its application over digits is common in bank checks, mail and historical document processing, among others. This paper presents an algorithm for segmentation of connected handwritten digits based on the selection of feature points, through a skeletonization process, and the clustering of the touching region via Self-Organizing Maps. The segmentation points are then found, leading to the final segmentation. The method can deal with several types of connection between the digits, having also the ability to map multiple touching. The proposed algorithm achieved encouraging results, both relating to other state-of-the-art algorithms and to possible improvements.     

一种地形图粘连汉字提取算法

地形图中包含了大量的字体丰富的汉字注记,其中有一部分由于与其它图符对象相互粘连而使得对象的尺寸变大超过了预定的阈值或失去了原有的结构特性,大大增加了提取难度。本文提出了一种寻求最佳分割点的算法来去除粘连,提取汉字的算法,取得了良好的效果。首先,在已提取出的汉字周围确定一个局部搜索范围,当局部范围内存在大尺寸的对象时表明可能有潜在的粘连汉字;其次,以图象中的分枝点和端点为顶点,以其中的图段为边建立对应的图;然后,在图中寻找最佳分割点,将图符分割成不同的互相分离的几个部分;最后,用连通成分结构分析的方法来提取汉字。     

Feature sensitive re-sampling of point set surfaces with Gaussian spheres

Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many computer graphics and geometric modeling applications.Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere,an adaptive re-sampling framework for point set surfaces is presented in this paper,which includes a naive sampling step by index propagation and a novel cluster optimization step by normalized rectification.Our proposed re-sampling scheme can generate non-uniformly distributed discrete sample points for the underlying point sets in a feature sensitive manner.The intrinsic geometric features of the underlying point set surfaces can be preserved efficiently due to our adaptive re-sampling scheme.A novel splat rendering technique is adopted to illustrate the efficiency of our re-sampling scheme.Moreover,a numerical error statistics and surface reconstruction for simplified models are also given to demonstrate the effectiveness of our algorithm in term of the simplified quality of the point set surfaces.     

A fast registration algorithm of rock point cloud based on spherical projection and feature extraction

Point cloud registration is an essential step in the process of 3D reconstruction. In this paper, a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP) algorithm. In our proposed algorithm, the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates, then image features are extracted and edge points are removed, the features used in this algorithm is scale-invariant feature transform (SIFT). By analyzing the corresponding relationship between digital images and 3D points, the 3D feature points are extracted, from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling, the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP) algorithm. Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.     

基于0-1规划的规则中文文件碎片自动拼接技术

为了实现规则中文文件碎片的拼接,研究了规则碎片文件中汉字文本的特征,提出了文件碎片中文本行信息的提取方法,定义了基于L1-norm 的碎片边界差异度概念,建立了基于0-1规划的文件碎片拼接模型,并运用聚类分析降低了算法复杂度。与现有同类算法相比,本文的算法无需使用人工干预即可完成正确拼接。     

融合图像显著性与特征点匹配的形变目标跟踪

目的 针对目标在跟踪过程中出现剧烈形变,特别是剧烈尺度变化的而导致跟踪失败情况,提出融合图像显著性与特征点匹配的目标跟踪算法。方法 首先利用改进的BRISK（binary robust invariant scalable keypoints）特征点检测算法,对视频序列中的初始帧提取特征点,确定跟踪算法中的目标模板和目标模板特征点集合;接着对当前帧进行特征点检测,并与目标模板特征点集合利用FLANN（fast approximate nearest neighbor search library）方法进行匹配得到匹配特征点子集;然后融合匹配特征点和光流特征点确定可靠特征点集;再后基于可靠特征点集和目标模板特征点集计算单应性变换矩阵粗确定目标跟踪框,继而基于LC（local contrast）图像显著性精确定目标跟踪框;最后融合图像显著性和可靠特征点自适应确定目标跟踪框。当连续三帧目标发生剧烈形变时,更新目标模板和目标模板特征点集。结果 为了验证算法性能,在OTB2013数据集中挑选出具有形变特性的8个视频序列,共2214帧图像作为实验数据集。在重合度实验中,本文算法能够达到0.567 1的平均重合度,优于当前先进的跟踪算法;在重合度成功率实验中,本文算法也比当前先进的跟踪算法具有更好的跟踪效果。最后利用Vega Prime仿真了无人机快速抵近飞行下目标出现剧烈形变的航拍视频序列,序列中目标的最大形变量超过14,帧间最大形变量达到1.72,实验表明本文算法在该视频序列上具有更好的跟踪效果。本文算法具有较好的实时性,平均帧率48.6帧/s。结论 本文算法能够实时准确的跟踪剧烈形变的目标,特别是剧烈尺度变化的目标。     

Detection of text regions from digital engineering drawings

An algorithm for text/graphics separation is presented in this paper. The basic principle of the algorithm is to erase nontext regions from mixed text and graphics engineering drawings, rather than extract text regions directly. This algorithm can be used to extract both Chinese and Western characters, dimensions, and symbols and has few limitations on the kind of engineering drawings and noise level. It is robust to text-graphics touching, text fonts, and written orientations     

基于凸包像素比特征的粘连汉字切分

汉字切分正确与否直接影响了汉字识别系统的识别率,粘连汉字则是切分中的难点。本文将基于背景细化的切分方法应用于《四库全书》的两字符粘连汉字数据集,并针对其中切分路径选择问题,提出了一种新特征- 凸包像素比,反映了在不同切分路径下汉字结构变化的特性。实验结果表明该特征对多种分类器均能有效地提高切分路径选择的正确率。其中在使用基于高斯混合模型分类器时取得了8816 %正确率。     

基于结构聚类和笔画分析的粘连手写汉字切分

粘连手写汉字的切分是手写汉字切分中亟待解决的问题之一。因此,针对粘连手写汉字提出一种新的切分算法。该算法首先通过寻找分界线的方法来提取粘连笔段,分界线的位置是通过粘连汉字骨架图像的聚类和笔段端点类属可信度的信息来确定的。然后提取粘连笔段并对其进行分析和类型（直线或曲线）判定,从而确定切分点及切分方向。最后利用背景细化算法找到分割曲线。该算法不仅能够很好地适用于两个粘连汉字宽窄不一、含有多个粘连点等粘连情况,而且具有良好的抗噪声效果。     

Handwritten Chinese text editing and recognition system

This paper describes a handwritten Chinese text editing and recognition system that can edit handwritten text and recognize it with a client-server mode. First, the client end samples and redisplays the handwritten text by using digital ink technics, segments handwritten characters, edits them and saves original handwritten information into a self-defined document. The self-defined document saves coordinates of all sampled points of handwriting characters. Second, the server recognizes handwritten document based on the proposed Gabor feature extraction and affinity propagation clustering (GFAP) method, and returns the recognition results to client end. Moreover, the server can also collect the labeled handwritten characters and fine tune the recognizer automatically. Experimental results on HIT-OR3C database show that our handwriting recognition method improves the recognition performance remarkably.     

基于骨架点分布规律的汉字笔段提取算法

提出了一种提取汉字笔段的新方法。从形态学骨架算法生成的骨架点出发,通过分析骨架点的半径分布及不同半径骨架点的位置,发现了笔段提取中产生的毛刺和畸变与骨架点半径之间的规律,进而以此规律为基础提出了一种克服毛刺和畸变的汉字笔段提取方法,最后给出了手写体和印刷体汉字笔段提取的实验结果。实验表明,该方法是行之有效的。     

基于特征约束点的纹理映射算法

纹理映射技术用于生成物体表面的纹理细节,是真实感图形技术的重要组成部分,也是计算机图形学的一个重要研究方向.针对目前很多纹理映射算法计算量大,方法比较复杂的缺点,应用Candide3作为三维网格模型,提出了一种快速有效的基于特征约束点的纹理映射算法.通过在三维网格模型和纹理图像上选取少量对应的特征约束点,利用三角网格剖分算法在纹理图像上建立选取特征点的三角网格.进而通过求取质心坐标的方法计算出三维网格模型上所有特征点的纹理坐标并完成整个三维网格模型的纹理映射.实验结果表明,提出的算法计算速度较快,能够得到高真实度的纹理映射效果,并且适用于不同纹理图像映射到同一三维网格模型上.     

动态水面数据采集与重建方法

自然现象模拟是计算机图形学中的一个重要研究热点.如何快速逼真地模拟自然现象是此类研究的主要目的.传统的解决思路大多采用基于物理的建模方法,而随着采集设备的快速发展,基于采集图像的重建方法得到了广泛关注与研究.本文以液体为研究对象,总结了近年来基于采集图像的重建方法的部分研究成果.针对动态水面,提出了一种动态水面数据采集与重建方法.首先,设计并搭建一套基于多相机的动态水面数据采集系统,采集得到多视图下不同水面运动现象的连续帧图像.其次,通过提取采集图像序列中每幅图像的亚像素级特征点,进行特征点匹配并建立特征点与物理空间中三维点的映射关系.然后,结合水介质的光学折射特性迭代求解水面上三维点阵的高度场和法向量.最终获得动态水面的重建结果.实验结果表明该方法能快速生成与采集水面可视效果相近的三维重建结果,可在计算机游戏、医学、科学研究可视化等领域具有应用前景.     

A Local Deviation Constraint Based Non-Rigid Structure From Motion Approach

In many traditional non-rigid structure from motion (NRSFM) approaches, the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models. Aimed at solving this issue, a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation. In our work, an effective model is first constructed with two terms: the overall estimation error, which is computed by a linear subspace representation, and a constraint term, which is based on the variance of the reconstruction error for each frame. Furthermore, an augmented Lagrange multipliers (ALM) iterative algorithm is presented to optimize the proposed model. Moreover, a convergence analysis is performed with three steps for the optimization process. As both the overall estimation error and the local deviation are utilized, the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points. Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm.