一种直线提取的新方法

直线提取是版面分析中的一个重要过程。该文提出了基于Hough变换的结构元素构造方法(HTSEC)以及基于数学形态学的直线提取方法(MMLE)，即首先将灰度文档图像利用半色调技术转换为二值图像，利用Hough变换检测直线倾斜角度以动态构造结构元素，然后利用数学形态学方法提取文档图像中的直线。     

基于遥感分类与数学形态学的道路信息提取

利用遥感图像获得完整道路信息网一直是信息提取的热点和难点,研究了结合遥感分类和数学形态学提取道路,以期获得满意的结果。遥感分类能有效区分人工地物与自然地物,因此能避免自然地物对提取的影响;数学形态学中的腐蚀和膨胀算子对地物边缘很敏感,一直是边缘信息提取的主要方法。实验表明该思路能快速有效提取道路信息。     

基于几何主轴和Hough变换的超宽带SAR二值边缘图像细化方法

从经典形态学细化方法研究出发,结合超宽带SAR图像特点和超宽带SAR图像道路提取的应用背景,提出了一种基于几何主轴和Hough变换的超宽带SAR图像边缘细化方法.该方法克服了经典形态学细化方法边缘细化中容易出现的毛刺现象和单纯Hough变换对于区域方向判定的误差,提高了道路边缘细化的完整性.其细化后的线基元形态良好,有利于线基元连接成完整的道路.     

形态学在道路图像边界检测中的应用

在道路图像边缘检测中,为实时、有效地从获取图像中提取出路面边界,研究了一种结合数学形态学的道路图像边界检测算法。形态学是一种从集合的角度来刻画和分析图像的图像处理和分析理论,其算子的性能主要以几何方式进行刻画,更适合于视觉信息的处理和分析。对采集到的原始道路图像经由图像预处理、道路边缘检测后,应用特殊结构元素对得到的二值图像进行形态学处理。保障有用信息比例的同时,去除了大量冗余和干扰信息,减少了边缘信息总量,减轻了后续运算的压力。最后采用Hough变换对腐蚀后图像进行处理,以实现道路边界的精准定位。和未采用腐蚀运算而直接对初始边缘检测后图像进行Hough变换的效果相比,能精准、有效地提取出道路边界线,并且大大地提高了算法的实时性。     

基于Hough变换和路径形态学的城区道路提取

针对高分辨率遥感影像上城区道路的特点,提出一种基于Hough变换和路径形态学的高分辨率遥感影像城区道路提取方法。用Hough变换检测主要道路的方向及长度,建立邻接图,根据邻接图确定路径的方向和长度,将这些确定的路径看作狭长但并非完全笔直的结构元素。实验结果表明,该方法不仅能够检测出直线道路信息,还能检测出宽的弯曲的道路及细长的曲率比较小的道路信息。     

文档图象的版面分析-基于数学形态学的方法

提出了基于 Hough变换的结构元素构造方法 (HTSEC)以及基于数学形态学的分割方法 (MMS) ,即首先将灰度文档图象利用半色调技术转换为二值图象 ,利用 Hough变换动态构造结构元素 ,然后利用数学形态学方法对版面进行划分并将其分为文字区、图形区和图象区     

基于数学形态学的虹膜定位算法

针对现有的虹膜定位算法的局限性,提出了一种基于数学形态学的虹膜定位算法。内圆定位是利用二值图像形态学的方法提取瞳孔的圆心和半径。外圆的定位用形态学进行边缘提取与Hough变换相结合,确定外圆圆心及半径。实验证明了该算法的合理性和有效性。     

基于数学形态学的虹膜定位算法

针对现有的虹膜定位算法的局限性,提出了一种基于数学形态学的虹膜定位算法。内圆定位是利用二值图像形态学的方法提取瞳孔的圆心和半径。外圆的定位用形态学进行边缘提取与Hough变换相结合,确定外圆圆心及半径。实验证明了该算法的合理性和有效性。     

文档图象的版面分析一基于数学形态学的方法

提出了基于Hough变换的结构元素构造方法(HTSEC)以及基于数学形态学的分割方法(MMS)，即首先将灰度文档图象利用半色调技术转换为二值图象，利用Hough变换动态构造结构元素，然后利用数学形态学方法对版面进行划分并将其分为文字区、图形区和图象区．     

利用动态结构元素提取直线

研究了灰度文档图像中提取直线的方法．首先将灰度文档图像利用半色调技术转换为二值图像，进行倾斜矫正后利用Hough变换动态构造结构元素(HTSEC)；然后根据这些结构元素，利用数学形态学中的击中／击不中变换提取文档图像中的直线(MMLE)。     

Real-time CAM-based Hough transform algorithm and its performance evaluation

Abstract. This paper proposes a highly parallel Hough transform algorithm for real-time straight-line extraction and its hardware implementation on a content-addressable memory (CAM). To achieve high-speed processing, incrementation for voting, which composes the Hough transform, and calculations for coordinate updating are carried out for the every scan line, not every edge pixel, and extracting maxima in Hough space is executed by parallel comparing. Moreover, variously weighted voting achieves more accurate line extraction in spite of the quantization error and noise in the image space. In the implementation, the CAM acts as a PE (processing-element) array that effectively performs highly parallel processing for the Hough transform and also as a memory for two-dimensional Hough space, and both voting and peak extraction are directly executed by the CAM. Evaluations of CAM hardware size, processing time and the accuracy of line extraction show that a real-time and high-resolution Hough transform for a 256256 picture can be achieved using a single CAM chip with current VLSI technology. This CAM-based Hough transform algorithm promises to be an important step towards the realization of a real-time and compact image-understanding system. Received: 15 August 1998 / Accepted: 15 March 2000     

改进的机场停泊飞行器目标分割算法

考虑到飞行器目标在整幅图像中所占的比例往往较小,且图像背景复杂,本文提出了一种基于机场区域提取的飞行器目标分割算法.该算法首先利用Hough变换检测直线的特性,定位机场跑道和停机坪的位置,并结合教学形态学等图像处理技术去除了非机场区域;在提取机场区域后,再选择适当的阈值对图像进行分割,最后经过形态学去噪、小区域去除等步骤分割出飞行器目标.实验结果表明,该算法改进了以往机场区域提取算法保留候机楼等附属部分以及提取结果中存在机场区域以外区域的缺点,较好地实现了机场停泊飞行器目标的分割,为下一步准确识别飞行器类型奠定了基础.     

基于Hough变换圆检测的人眼精确定位方法

眼睛定位是人脸识别的非常重要的一个环节,后续的图像几何归一化、图像矫正和特征提取都依赖于眼睛的位置。论文提出一种人眼快速定位的新方法。首先用积分投影法检测到眼睛瞳孔的大致位置;然后用Sobel边缘检测算子提取图像边缘信息;再利用基于圆的Hough变换快速定位出人眼,实验证明了该方法的有效性。     

Vision-based autonomous land vehicle guidance in outdoor road environments using combined line and road following techniques

An intelligent approach to autonomous land vehicle (ALV) guidance in outdoor road environments using combined line and road following and color information clustering techniques is proposed. Path lines and road boundaries are selected as reference models, called the line-model and the road-model, respectively. They are used to perform line-model matching (LMM) and road-model matching (RMM) to locate the ALV for line and road following, respectively. If there are path lines in the road, the LMM process is used to locate the ALV because it is faster than the RMM process. On the other hand, if no line can be found in the road, the RMM process is used. To detect path lines in a road image, the Hough transform is employed, which does not take much computing time because bright pixels in the road are very few. Various color information on roads is used for extracting path lines and road surfaces. And the ISODATA clustering algorithm based on an initial-center-choosing technique is employed to solve the problem caused by great changes of intensity in navigations. The double model matching procedure combined with the color information clustering process enables the ALV to navigate smoothly in roads even if there are shadows, cars, people, or degraded regions on roadsides. Some intelligent methods to speed up the model matching processes and the Hough transform based on the feedback of the previous image information are also presented. Successful navigations show that the proposed approach is effective for ALV guidance in common roads. ©1997 John Wiley & Sons, Inc.     

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。     

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     

基于 Hough 变换和 Catmull-Rom 样条曲线的 钢轨检测算法

针对现有的钢轨检测算法中对于钢轨识别准确性不高的问题,提出一种基于 Hough 变换和 Catmull-Rom 样条曲线的钢轨检测算法。首先对图像进行降噪预处理,利用 Canny 边缘 检测算法进行边缘检测,然后利用 Hough 变换检测直轨,根据直轨消失点确定轨道模型,最后 针对弯轨利用等分窗口搜索弯轨特征点,对选取的特征点进行 Catmull-Rom 样条曲线拟合弯轨。 实验表明,该算法不仅能够准确拟合钢轨曲线,而且具有较好的准确性和鲁棒性。     

参数空间分解法

Ｈｏｕｇｈ变换是目前文献中应用最广泛的特征提取方法。然而,Ｈｏｕｇｈ变换空间开销大的缺陷严重地限制了它的进一步应用。空间开销大的缺陷不仅在标准Ｈｏｕｇｈ变换中存在,而且在近年来新提出的随机Ｈｏｕｇｈ变换、概率Ｈｏｕｇｈ变换以及动态Ｈｏｕｇｈ变换中同样存在。这一缺陷在３０多年来的Ｈｏｕｇｈ变换研究过程中始终没有得到很好的解决。该文提出的参数空间分解法旨在从根本上克服Ｈｏｕｇｈ变换空间开销大的缺陷。参     

Improved Lane Line Detection Algorithm Based on Hough Transform

In order to simplify the lane line detection algorithm based on Hough transform, we propose an algorithm directly identifying lane line in Hough space. The image is conducted with Hough transform, and the points conforming to the parallel characteristics, length and angle characteristics, and intercept characteristics of lane line are selected in Hough space. The points were directly converted into the lane line equation. Also, the lane lines are conducted with fusion and property identification. The experimental results showed that the lane can be better identified on expressways and structured roads. Compared with tradition algorithm, the identification is effectively improved.