图像匹配技术研究

图像匹配技术研究(论文17000字)
摘要：图像匹配是以特征匹配和灰度匹配为主流算法，在不同图像之间找出空间和位置关系，并通过一致性和相似性来分析和寻求相似影响目标的技术，目前已经被广泛应用于遥感、医学成像、全息影像创作等方面。随着图像识别及重建领域的不断发展、图像识别技术在图像融合、镶嵌、目标跟踪识别等领域扮演着越来越重要的角色。灰度匹配算法又可分为相关系数法和最小误差法。本文改进的相关系数法，在经过实验仿真和优劣分析后证明，在保证了匹配精度的前提下，既能提高匹配速度，又能减少匹配过程中的计算复杂度，提高了匹配效率，同时也满足了实际应用的实时性要求。对于基于边缘特征的算法匹配步骤中，针对边缘检测时使用的Canny算子使用了引进Otsu 算法自适应地根据图像灰度生成高低阈值，使得在噪声环境下，改进后的Canny算子比传统的Canny算子性能更优，检测到的细节更多，更有利于图像的匹配效率。
关键词：图像匹配；灰度；边缘特征；相关系数法；边缘检测；Canny算子

Research on image matching technology
Abstract: Image Matching is a mainstream algorithm that uses feature matching and gray-scale matching to find spatial and positional relationships between different images, and analyzes and seeks similar influences on the target through consistency and similarity. Is widely used in remote sensing, medical imaging, holographic image creation and so on. With the continuous development of image recognition and reconstruction, image recognition technology plays an increasingly important role in image fusion, mosaic, target tracking and other fields. Gray-scale matching algorithm can be divided into correlation coefficient method and minimum error method. The improved correlation coefficient method in this paper proves that it can not only improve the matching speed, but also reduce the computational complexity in the matching process and improve the matching efficiency. At the same time, it also meets the real-time requirements of practical applications. For edge-based algorithm matching steps, the Canny operator used for edge detection uses the introduction of Otsu algorithm to adaptively generate high and low thresholds based on the image grayscale, making the improved Canny operator better than traditional ones in a noisy environment. The Canny operator has better performance, more details are detected, and it is more conducive to image matching efficiency.
Key words: Image matching; Grayscale; Edge feature; Correlation coefficient method; Edge detection; Canny operator

目 录
引言    1
1 绪论    1
1.1 课题研究背景及意义    1
1.2 课题研究内容    2
1.3 课题研究现状    2
2 图像匹配技术概述    3
2.1 图像匹配概念    3
2.2 图像匹配的数学表达    3
2.3 图像匹配的流程和关键要素    4
2.3.1 图像匹配的流程    4
2.3.2 图像匹配的关键要素    4
3 图像匹配相关算法介绍与实现    5
3.1 基于像素灰度相关的匹配算法    5
3.1.1 基于像素灰度相关的匹配算法介绍    5
3.1.2 基于像素灰度的匹配算法的实现与结果分析    7
3.2 基于边缘特征的匹配算法    12
3.2.1 基于边缘特征的匹配算法介绍    12
3.2.2 基于边缘特征的匹配算法的实现与结果分析    16
4 基于像素灰度相关的匹配算法的改进    21
4.1 针对相关系数法的改进基本原理    21
4.2 针对相关系数法的改进的实现    22
5 基于边缘特征的匹配算法的改进    26
5.1 Canny算子的缺陷分析    26
5.2 对边缘特征匹配算法的改进    27
5.3 以改进的Canny算子为基础的匹配算法仿真实现    28
6 总结    31
参考文献    32
致谢    33