数字图像处理角点检测方法研究毕业论文

数字图像角点特征检测方法研究

目录

引言 (3)

1 研究背景与发展 (6)

1.1研究背景 (6)

1.2研究现状和发展概述 (6)

1.3应用软件M ATLAB (7)

2 角点检测概念与原理 (9)

2.1角点的定义 (9)

2.2角点概念及特征 (9)

2.3角点检测意义 (9)

2.4角点检测原理 (10)

2.5角点检测技术的基本方法 (10)

2.5.1 基于模板的角点检测 (10)

2.5.2 基于边缘的角点检测 (11)

2.5.3 基于灰度变化的角点检测 (13)

3 角点算法概述 (14)

3.1角点检测的标准 (14)

3.2H ARRIS角点检测算子 (14)

3.2.1 Harris角点检测算子流程图 (19)

3.2.2 Harris角点检测算子的特点 (20)

3.2.3 Harris角点检测性质 (20)

3.2.4 Harris和Moravec算子角点检测实验结果 (21)

3.3一种改进的H ARRIS的算法 (23)

3.3.1试验结果 (24)

3.4S USAN角点检测算子 (25)

3.3.1 SUSAN角点检测一般步骤 (27)

3.3.2 Susan角点检测算子特点 (29)

3.3.3 Susan角点检测试验结果 (29)

4 其他算子简介 (33)

4.1小波变换算子 (33)

4.2F ORSTNER算子 (33)

4.3CSS角点检测算法 (35)

4.4ACSS角点检测算法 (36)

4.5各种角点检测算法的比较 (36)

结论 (39)

致谢 (41)

参考文献 (42)

附录1 HARRIS算法程序 (44)

附录2 MORA VEC算法程序 (46)

附录3 改进的HARRIS算法 (48)

附录4 SUSAN算法程序 (50)

本文主要研究了数字图像的角点特征检测方法，应用了Matlab软件对图像进行处理。在计算机视觉中、机器视觉和图像处理中，特征提取都是一个重要的方向。角点决定了图像中目标的形状，这是一个重要的特征。此特征应用于很多领域，例如运动估计、目标描述、图像匹配、目标跟踪等，因此提取角点的意义很重要。角点含有很大的信息量，对图像处理有足够的约束力。这使运算量降低，有效的提高运算速度。角点检测问题是图像处理领域的一个基础问题,是低层次图像处理的一个重要方法。角点检测的目的是为了匹配,而匹配的效率取决于角点的数量。

通过应用MATLAB编写程序，本文编辑有Harris算法、Susan算法、Moravec算和改进的Harris算法的程序。通过MATLAB运行得出图像提取角点的结果，分析了各种算法的优缺点。Harris角点检测原理中角点与自相关函数的曲率特性有关。描述了局部图像灰度的变化程度是自相关函数。根据邻近像素灰度相似度这个概念提出改进后的算法。Moravec角点检测算法思路简单,计算过程易于实现,判断条件少。SUSAN角点检测算法直接利用图像灰度相似性的比较,而不需计算梯度。

关键词：

角点；图像比配；检测；图像处理；Harris算子

This paper mainly studied the method of digital image feature detection and processed image by the Matlab. In computer vision, machine vision and image processing, this was an important direction for feature extraction. The corner point determines the shape of the target image, which was an important characteristic.The characteristic applied to a wide variety of domain, such as motion estimation, goal description, image matching, target tracing and so on. Therefore corner points were extracted that was important meaning. The corner points had a large number of information that could provide enough constraints on image processing. This marked the computation reduction, effectively improved the computing speed. Corner detection was a question. In the field of image processing, the corner detection was bases question, and was an important method in low level image processing. Corner detection was designed to match. The matching efficiency depended on the number of corner points.

By means of the use of MATLAB to wrote program. This article editing program had Harries algorithm, Susan algorithm, modified harries algorithm, Moravec algorithm. By running the MATLAB reached image to extract corner point results, and analyzed the advantages and disadvantages of various algorithms. In Harris’ corner detection principle, corner point was related to the curvature properties of the autocorrelation function. The auto correlative function described the local change of image gray degree.According to adjacent pixel gray level’s similarity, which came up with improved algorithm.Moravec’s corner detection algorithm had Simple way of thinking, and counting process was apt to come true, and had very less judging criteria. Susan’s corner detection algorithm based on comparing the image gray similarity, and didn’t need to calculate the gradient.

Keywords:

Angular point；Image match；Detection；Image processing；Harris operator