摘要：在数字图像处理领域中，常常需要对场景中的同心圆图像进行检测，通过检测同心圆来定位及识别物体可以大大提高生活与生产智能化水平，节省人力和时间成本。在图像检测中，圆的检测采用的方法常见的有hough变换方法，该方法把图像空间的检测问题转换到参数空间。尽管hough变换法在直线等低文参数空间检测中有着很理想的效果, 但随着参数文数的增多,所需计算量越来越大。所以对于同心圆这种特殊的多圆检测问题, 经典的hough变换法有其局限性。为此，Xu Lei[1][2]等人提出了随机hough变换，它通过在图像空间中随机选取不共线的三个点映射成参数空间的一个点，是多对一的映射，从而避免了传统hough变换一到多映射的巨大计算量。
本文给出了一种基于随机hough变换的同心圆检测方法，降低了检测的计算复杂度。本文给出了该方法的实现步骤,并结合应用实例进行了仿真分析。
毕业论文关键词： hough变换；随机hough变换；圆；同心圆；图像处理
Concentric circles detection based on the detection Hough transform
Abstract: In the field of digital image processing, it is often need to detect the concentric circles in the scene, by detecting the concentric circles to locate and identify objects can greatly improve the living and production intelligence level, saving manpower and time costs.Hough transform is the method of detecting the circle in the image detection. It detected problems with converting image space to parameter space. Although hough transform has a very good effect for the linear low-dimensional parameter space detector , but with the increase in parameters dimension, increasing the amount of computation required. So for this particular concentric circle detection problem, the classic hough transform method has its limitations. To this end, Xu Lei[1][2], who proposed random hough transform it by a point in the image space randomly selected non-collinear three points mapped into the parameter space, that is many-to-one mapping. And thus avoiding the traditional hough transform a the enormous amount of calculations caused by one-to-many mapping.
  This paper presents a concentric circle detection method based on random hough transform and it reduces the computational complexity of detection. This paper presents the implementation steps of the method, and application examples are simulated and analyzed.
Keywords:  Hough transform ; Random hough transform ; Circle ; concentric circles ; image processing
目   录
1 绪论    1
1.1 问题的提出与研究意义    1
1.2 同心圆检测算法的研究现状    2
1.3 本文主要研究内容    3
2 图像处理预知识    4
2.1 数字图像处理    4
2.2 图像灰度化    5
2.3 图像去噪    6
2.3.1 图像噪声    6
2.3.2 图像滤波    7
3 图像分割    12
3.1 图像分割定义    12
3.2 图像分割算法    13
3.2.1 阈值分割法    13
3.2.2 区域生长法    14
3.2.3 边缘分割法    15
4 边缘检测    17
4.1 边缘检测定义    17
4.2 边缘检测原理    17
4.3 边缘检测方法    18
4.3.1 Roberts算子    18
4.3.2 Prewitt算子    19
4.3.3 Sobel算子    19
4.3.4 Laplacian边缘算子    20
4.3.5 Canny算子    20
4.3.6 Log边缘算子    21
4.4 本章小结    22
5 hough变换    24
5.1 hough变换基本原理    24 基于Hough变换的同心圆检测+代码:http://www.youerw.com/jisuanji/lunwen_31996.html