基于直线段跟踪的手绘图元离线识别方法*

手绘图形是人类思维外化和表达意图的一种有效方式,如何有效地提取手绘在图纸上的图形元素是理解绘图者意图的关键问题。鉴于手绘图形是由基本图元组合构成,采用层次结构逐步实现图元提取的思想,提出了一种手绘基本图元（线段、弧、圆和椭圆）的离线识别方法。在提取图形笔画骨架像素的基础上,跟踪骨架像素得到图形的直线段描述;通过对直线段序列的分析,进行直线段序列的断开和连接处理,形成图元的曲线段描述,通过对图元曲线段描述的分析得出图元的几何参数。实验表明,该方法能够以高精确度快速识别出图像中包含的手绘图元,具有良好的稳定性     

基于趋势优先跟踪方向的直线矢量化算法

直线是工程图纸中最常使用的图元，并且工程图中常出现的圆、圆弧、曲线都可以用直线段来生成，因此，对直线的矢量化是工程图识别中一个非常重要的研究方向。针对工程图的特点，提出了一种基于趋势优先跟踪方向的直线矢量化算法，在细化后的单像素宽度图形上，根据直线的斜率趋势确定优先跟踪方向，较好地解决了在交叉点处长直线断裂的问题。同时结合跟踪过程中使用的边跟踪边检测方法，可以有效抑制短小直线段过多地产生，实现小斜率直线和大斜率直线的整体识别。     

图纸矢量化中的线弧分离与识别

图纸矢量化是现代图像图形处理技术发展的一个重要分枝,具有很高的研究价值及广泛的应用前景。本文针对图纸矢量化中的图元识别技术,提出了系统的基于线弧分离和拟合识别方法。通过长直线和圆弧分离、尖角与弧结束判定及反向相切弧分离,进而识别多种基本图无了。理论分析和实验结果均表明,该方法对于不同图纸中线弧图元可以进行有效的识别。     

基于局部Hough变换的银行支票参考基线自动定位算法

直线自动检测有许多重要的应用,Hough变换是图象中直线检测的一个常用算法.但Hough变换的一个重要缺点是只能检测直线,而不能检测直线段.本文基于局部Hough变换,研究银行支票识别中,参考基线段的定位问题,给出了一种高效准确的参考基线定位算法.     

宽线段Hough变换及其在箭靶识别上的应用

Hough变换是用于检测图像中直线段的有力工具。论文提出的宽线段Hough变换针对传统Hough变换进行了改进,使之适用于多条宽线段同时存在的情况,并且解决了端点提取的问题。该方法应用于箭靶识别取得了很好的效果,实验表明对比传统方法具有较大优势。     

线状图形矢量化中的行段分析与后处理

针对目前工程图纸识别中存在的几项难点技术,提出基于行程编码的矢量化方法, 对小弯度直线的纠正问题、特征线段的延伸与合并算法进行了研究.同时使用图像局部形态 分析的方法,直接在原图像上进行识别,保证了图像信息的完整性和可检测性.该项研究结果 在建筑工程图纸的自动识别及自动计算中得到了较好的应用.     

基于链码检测的直线段检测方法

直线是图像的重要特征，直线参数是进行图像识别和直线段三维重建的重要基础数据。基于链码检测的直线段的检测方法分为4步：以边缘图像为基础进行链码检测；根据链码估计曲率，检测链码角点，并在角点处拆分链码；通过链码直方图检测直线链码；对直线链码进行直线参数估计，并根据连接准则，进行直线连接。实验证明，该方法可以对直线段进行有效的检测。     

彩色基本图元反走样算法

本文基于双像素扫描转换原理,研究了基本图元直线、圆和椭圆的反走样算法,特别是直线的动态反走样算法。针对彩色线条,本文应用了反走样颜色处理技术。通过绘制反走样“卫星图”对所提算法进行了验证。     

识别直线链码的一种新方法

目前对直线链码的识别方法是判断被识别的链友理满足直线链码应满意的三个条件。本文提出一个全新的识别方法。其基本思想是先确定被识别链码所对应线段的两端点，然后用直线绘制算法在这两个端点之间直线路径产生点，同时与被识别链码的相应码值所指定的点进行比较。如果相同，则为直线码；否则，就不是直线链码，该方法有实现现简单和执行速度快等优点。     

图纸图像上线条的交互拾取

目前图纸的处理技术可以分为两类：矢量经处理和图像级处理。两者都 存在各自很 难解决的问题：矢量化的准确度不高，图像级的交互操作又不方便。本文提出在图纸图像上直接拾取直线的思想来源于两者的有要结合，极大地方便了图像级的交互操作。它的基本思想是：从直线上一点开始，连续扫描跟踪直线的走向，在跟踪的过程中，不断用抽样点校正直线方程；在直线地其它图元的交叉点处，删除该线后进行专门的交叉点恢复，以使其它图元的     

Relational object recognition from large structural libraries

This paper presents a probabilistic similarity measure for object recognition from large libraries of line-patterns. We commence from a structural pattern representation which uses a nearest neighbour graph to establish the adjacency of line-segments. Associated with each pair of line-segments connected in this way is a vector of Euclidean invariant relative angle and distance ratio attributes. The relational similarity measure uses robust error kernels to compare sets of pairwise attributes on the edges of a nearest neighbour graph. We use the relational similarity measure in a series of recognition experiments which involve a library of over 2500 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 94%. A comparative study reveals that the method is most effective when either a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms the standard and the quantile Hausdorff distance.     

一种包括隐含图形信息提取的工程图识别算法

工程图识别应该模仿人阅读工程图，把隐含图形信息提取出来，文中提出一种由稀疏的行程段直接提取整条图线，包括隐含图形信息提取的工程图识别算法，该算法首先提取整条图线的信息，再确定图线交点，通过分析线素，间隔等的规律确定线型，最后，基于工程图知识对图线拓扑关系进行校正。     

New grouping and fitting methods for interactive overtraced sketches

This paper describes a new method for recognizing overtraced strokes to 2D geometric primitives, which are further interpreted as 2D line drawings. This method can support rapid grouping and fitting of overtraced polylines or conic curves based on the classified characteristics of each stroke during its preprocessing stage. The orientation and its endpoints of a classified stroke are used in the stroke grouping process. The grouped strokes are then fitted with 2D geometry. This method can deal with overtraced sketch strokes in both solid and dash linestyles, fit grouped polylines as a whole polyline and simply fit conic strokes without computing the direction of a stroke. It avoids losing joint information due to segmentation of a polyline into line-segments. The proposed method has been tested with our freehand sketch recognition system (FSR), which is robust and easier to use by removing some limitations embedded with most existing sketching systems which only accept non-overtraced stroke drawing. The test results showed that the proposed method can support freehand sketching based conceptual design with no limitations on drawing sequence, directions and overtraced cases while achieving a satisfactory interpretation rate.     

一种处理激光雷达数据的聚类分析方法

车载激光雷达所获取的程距数据有数据量大、数据流有序、噪声点多等特点,文中针对激光雷达数据的特点提出了一种带约束的自适应聚类分析方法.该方法通过人机交互输入参数以后,对激光雷达的扫描点进行自动聚类,通过迭代后得到相对光滑的线段,为道路障碍检测等后续工作提供了便利.最后还讨论了如何通过计算线段的位置关系,得到道路方向、障碍位置及大小等信息.通过对程距数据的实验表明,该方法能够达到预期的效果.     

Optimising the application of the Hough transform for automatic feature extraction from geoscientific images

We have adapted the Hough transform to extract linear features successfully from geoscientific datasets. The Hough transform is used in an automatic technique, which makes use of a parameter space to describe features of interest in images. This method has been widely applied in machine vision for recognition of features in highly structured images. Geoscientific data is more demanding. Features of interest within scenes of natural environments exist on all scales, are often partially obscured and the images are usually noisy. Pre-processing of images before the HT is essential. Adaptations of the HT to cope with particular properties of geoscientific data include: optimising the dimensions of the discrete transform domain; using feature-modelling to cancel lines found; transforming multi-scale tiles of the original image and correcting amplitudes in the transformed domain to account for the position of features. These specific adaptations produce a method for automatic feature detection which requires the user to select only two parameters. Output of the procedure is rich in feature content and accurate, leaving a clean result for statistical analysis. This optimised HT is robust for natural scenes, coping in particular with short line-segments.     

基于多重规则和路径评价的在线中英文手写识别方法

手写文本识别方法主要应用于文本输入技术,对人机交互领域的发展起关键作用。针对多数在线输入法无法识别中英文混合手写识别的问题,提出一种在线中英文混合手写文本识别方法。通过对文本笔画进行基于水平相对位置、垂直重叠率、面积重叠率规则的整合以及连笔切分,得到一系列字符片段,同时利用笔画个数、宽高比、中心偏离、平滑度等几何特征和识别置信度,对字符片段进行中英文分类。在此基础上,根据分类结果并结合自然语言模型的路径评价及动态规划搜索算法,分别对候选的中、英文字符片段进行合并处理,得到待识别的中、英文字符序列,并将其分别送入卷积神经网络的中、英文识别模型中,得到手写文本识别结果。实验结果表明,在线手写中英文混合文本识别正确率达93.67%,不仅能切分在线手写中文文本行,而且对包含字符连笔的在线手写中英文文本行也有较好的切分效果。     

A generic approach to recognising isolated, nested and interacting features from 2D drawings

Many useful three-dimensional (3D)-feature recognition systems have been developed over the past few decades. However there is a shortage of feature recognition systems in the two-dimensional (2D) domain. This has an adverse effect on the realisation of CADCAM benefits in Small and Medium Enterprises. Here is an attempt to redress this deficit. This paper presents a hint-based feature recognition system which recognises machining features from 2D orthographic projections through a two-stage process of profile searching and feature completion. Three types of profiles: Type I, II or III are defined in this system. Together they are sufficient to describe most profiles, if not all, in any 2D drawing. Profile searching identifies these profiles while feature completion establishes the cavity emanating from these profiles using a multi-level top–down approach. This process enables the system to deal with the isolated, nested and interacting features using a common framework. The nested features are handled by applying the same reasoning used for isolated features recursively while interacting features are decomposed automatically by the process of profile searching into their constituent isolated features.     

Offline handwritten Amharic word recognition

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their spatial relationships. The recognition system does not require segmentation of characters but requires text line detection and extraction of structural features, which is done by making use of direction field tensor. The performance of the recognition system is tested by a dataset of unconstrained handwritten documents collected from various sources, and promising results are obtained.