基于SIFT图像配准算法的研究

中国科学技术大学

硕士学位论文

基于SIFT图像配准算法的研究

姓名：汪道寅

申请学位级别：硕士

专业：通信与信息系统

指导教师：胡访宇

2011-05-12摘 要

I 摘 要

图像配准是一种寻找同一场景的两幅或多幅图像之间的空间变换关系、并对

其中的一幅或多幅图像进行变换的过程。图像配准是所有图像分析任务中最为关

键和基础的步骤，是图像拼接、图像重建、目标识别等应用的前提。对于常用的

基于特征的图像配准方法，其关键在于如何对特征进行有效的提取，尺度不变特

征（Scale Invariant Feature Transform，SIFT）算法能够为我们所提供需要的不变

特征。SIFT特征具有旋转、光照、仿射和尺度等不变性，SIFT算法是目前特征

检测和匹配算法中最为有效的算法。

本文以对尺度不变特征SIFT算法的研究为中心，首先以不变特征理论作为

背景，引出SIFT尺度不变特征的概念。尺度不变特征SIFT算法可以划分为特

征检测、特征描述和特征匹配三个部分，本文对每个部分的组成逐一进行了分析

和讨论，详细介绍了尺度不变特征SIFT算法的实现过程，对其所包含的匹配搜

索、聚类变换及内外点筛选等子算法的特点和性能分别做了深入研究。在特征检

测时，运用非极大值抑制方法来检测均匀性分布的特征点，通过设置标志位对检

测步长进行调整以减少检测次数；考虑到SIFT算法在设置距离比阈值时的局限

性，固定的参数设置不能适应所有图像，因此需要对SIFT算法匹配阶段的距离

比阈值进行参数寻优。本文以折半查找法作为基础，设计了一个能够满足要求的

寻优算法；对于检测到的特征点中偏差较大的匹配点，采用特征点对之间的特征

一致性的几何约束进行粗匹配剔除。实验表明，改进算法在性能上得到了提高。

继SIFT算法出现之后，陆续涌现了一系列的变种算法。本文对这些变种算

法也进行了研究，并进行了分析和对比实验。通过对比实验发现，虽然这些算法

较SIFT算法在某些方面的确有所改进，但是同时也损失了SIFT算法的其他性

能，如算法的使用范围、尺度不变特性以及算法的计算复杂度。因此，对SIFT 算

法进行深入研究仍然是必要的。目前SIFT算法改进和完善工作主要聚焦于提高

算法的计算效率、找到更精确的特征检测算法或更有效的特征描述子等方面。另

外，由于SIFT算法描述部分本身就受到生物神经方面的启发，后续研究考虑继

续将生物学的原理应用于SIFT算法的改进当中。同时，将SIFT算法运用到实

际生活当中，以解决更多的实际问题也是今后的研究重点。

关键词：图像配准 尺度不变特征 均匀性特征检测 参数自适应 特征一致性约束

Abstract

III ABSTRACT

Image registration is the process of trying to find the space transformation

between two or more images of the same scene and transforming one or more images

among them. Image registration is a crucial and basic step in all image anlysis tasks, it

is the precondition of the application of image mosaic, image reconstruction, object

recognition, to name a few. The common used method of image registration is the

method based on feature, the key of the method is how to detect features more

efficient. Scale invariant feature transform algorithm can provides us the invariant

features that we want, scale invariant features have rotation, illuminance, affine and

scale invariant, it is the most efficient algorithm of feature detection and matching.

This paper taked the research of Scale Invariant Feature Transform algorithm as

the center part. The first introduction part is the invariant feature theory as the

background to introduce the conception of scale invariant feature transform. Scale

invariant feature transform algorithm can be divided into three parts: features

detection, features description and features matching. This paper analysed and

discussed each parts of the algorithm, and gave a particular introduction on the

implementation procedure of the scale invariant feature transform algorithm,

including the researches on the character and performance of the algorithms like

hough tranform, best bin first tree retrieval and random sample consensus algorithm

and so on. At the step of feature detection, using Non-maximal suppression method to

detect well-distributed features and reducing detection times by setting the flag to

adjust detection step. Considering of the contraint of scale invariant feature transform

algorithm when it set the parameter of the thresh of the distance ratio, the setting fix

parameter don’t satisfy all images, so it’s necessary to find the best parameter of the

thresh in the step of features matching. This paper designs a simple algorithm to find

the optimization value based on binary search algorithm. For the points which have

big error in the detection feature points, they can be eliminated by feature consistent

geometry constrast between matching points. The experiments show the improved

algorithm has better performance than the preview one.

After the appearance of the scale invariant feature transform algorithm, on top of

other algorithms to improve the scale invariant feature transform algorithm. This

paper do some researches on these algorithms, and make some analysis and contrast Abstract

IV experiments. From the contrast experiments, it can be found that these algorithms

indeed make some progress in some parts of the algorithm, but they actually lost some

other performance in other aspects, like reducing the using range of the algorithm,

cutting down the scale invariant of the algorithm, or increasing the computation

complexity of the algorithm. So it is still necessary to do some deep research on the

algorithm. Now most of the job in improving and perfecting the algorithm are

focusing on the aspects of improving the algorithm computing efficiency and

providing more accurate feature algorithm or more available feature descriptor.

Besides, the description part of the algorithm was inspired by biology nerve, the

following research would continue take the biology principle into the improvement of

the scale invariant feature transform algorithm. At the same time, it is the research key

point to take the algorithm into the real life so as to solve more reality problems.