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Efficient generalized Hough transform algorithms for modern applications.

机译：适用于现代应用的高效广义霍夫变换算法。

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摘要

In this research, some novel modifications and modern applications of the Hough transform algorithms have been pursued. First, the dual points Hough transform algorithm was studied. Our approach optimises the R-Table of the prototype by using some statistics techniques in order to reduce the number of entries per index in the R-Table and make the entries inside the R-Table distribute evenly. Experimental results show that our method can both speed up the process and increase the recognition accuracy.; Second, a dominant point detection algorithm has also been studied. The reason for pursuing this study is that we can reduce the number of operations by employing dominant points as the features in the Hough transform process. Owing to the lack of appropriate dominant point detection algorithms for real scenes, a new dominant point detection algorithm has been proposed. Apart from the curvature points, we also defined the termination and intercept points on a digital curve as the dominant points since they indeed give useful information about the natures of the curves. A weighted mask is proposed for the initial detection. By using a look up table, all possible dominant points can be located efficiently. Experimental results show that our method achieves a better performance in terms of approximation error when compared with other methods.; After the development of the dominant point detection algorithm, we have developed a Hough transform algorithm that makes use of the dominant points as the corresponding features in tracking an object in a video sequence. The user inputs a user-defined object in the first frame. Then, an R-Table is built based upon the dominant points of the selected object. When the next frame is reached, the transformation parameters of the object can then be detected by using the Hough transform. Our proposed algorithm is capable not only to track the object under a continuous deformation, but also able to recognise the object under occlusion and recover the tracking when the object reappears in the video sequence.; Finally, a Hough transform algorithm that makes use of the color information as the corresponding features is studied. To start with, a color image is segmented into some regions with homogeneous colors by applying a watershed algorithm and a new region merging algorithm. The region merging algorithm merges regions based on the ideas of reducing the errors of the color differences, maintaining the uniformity of the color and controlling the minimum size of each region. (Abstract shortened by UMI.)
机译：在这项研究中，已经对霍夫变换算法进行了一些新颖的修改和现代应用。首先，研究了双点霍夫变换算法。我们的方法通过使用一些统计技术来优化原型的R表，以减少R表中每个索引的条目数量，并使R表中的条目均匀分布。实验结果表明，该方法既可以加快处理速度，又可以提高识别精度。其次，还研究了优势点检测算法。进行这项研究的原因是，我们可以通过将优势点作为霍夫变换过程的特征来减少运算次数。由于缺乏适用于真实场景的优势点检测算法，提出了一种新的优势点检测算法。除了曲率点之外，我们还将数字曲线上的终止点和截距点定义为主要点，因为它们确实提供了有关曲线性质的有用信息。提出了一种加权掩模用于初始检测。通过使用查找表，可以有效地定位所有可能的优势点。实验结果表明，与其他方法相比，该方法在逼近误差方面具有更好的性能。在开发了优势点检测算法之后，我们开发了一种Hough变换算法，该算法利用优势点作为视频序列中跟踪对象的相应功能。用户在第一帧中输入用户定义的对象。然后，基于所选对象的优势点构建R表。当到达下一帧时，可以使用霍夫变换来检测对象的变换参数。我们提出的算法不仅能够跟踪连续变形下的物体，而且能够识别出遮挡下的物体，并在视频序列中再次出现物体时恢复跟踪。最后，研究了一种利用颜色信息作为相应特征的霍夫变换算法。首先，通过应用分水岭算法和新的区域合并算法，将彩色图像分成具有均匀颜色的某些区域。区域合并算法基于减少色差误差，保持颜色均匀性并控制每个区域的最小大小的思想来合并区域。 （摘要由UMI缩短。）

著录项

  • 作者

    Chau, Chun Pong.;

  • 作者单位

    Hong Kong Polytechnic (People's Republic of China).;

  • 授予单位 Hong Kong Polytechnic (People's Republic of China).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 208 p.
  • 总页数 208
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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