基于改进SIFT的柱面全景图像拼接算法研究

西南科技大学硕士研究生学位论文II

Abstract

Image stitching is a process of registering and fusing multiple image sequences with certain overlapping regions and small viewing angles to form a wide-angle image with more information.Among them,panoramic image stitching as a main application direction of image stitching technology,has become a research hotspot in the field of computer vision and digital image processing.

This paper mainly studies the basic principles and key technologies of feature-based panoramic image stitching.Firstly,aiming at the problems of high complexity and low registration efficiency of traditional SIFT algorithm,a fast SIFT feature detection algorithm is proposed based on the overlapping region, which greatly reduces the computation time.Then,in the process of SIFT feature matching,the process of transform matrix parameter estimation is simplified by dividing the overlapping area into blocks,which reduces the number of iterations of feature matching.At the same time,by verifying the correctness of the transformation matrix,the correctness of subsequent feature point matching is ensured,and the matching efficiency is improved.Secondly,the stitching of dynamic scene images can not solve the exposure difference and fusion ghosting. Based on the idea of dynamic programming,this paper proposes an improved algorithm that expands the optimal seam search criteria combined with texture differences,which combined with a piecewise linear weighting to eliminate exposure differences and achieve ghost-free fusion.Finally,based on the improved algorithm proposed in this paper,the cylinder projection model is used to realize the cylindrical panoramic image stitching.Experiments show that this method not only eliminates ghosting,reduces the impact of exposure differences, but also improves computational efficiency.

Key words:Image stitching;SIFT algorithm;dynamic scene;optimal seam; Cylindrical projection

西南科技大学硕士研究生学位论文III

1绪论 (1)

1.1课题研究背景和意义 (1)

1.2国内外研究现状 (2)

1.2.1图像配准的研究现状 (2)

1.2.2图像融合的研究现状 (3)

1.3本文结构安排 (4)

2全景图像拼接相关理论及其关键环节 (5)

2.1图像采集 (5)

2.2图像预处理 (5)

2.2.1畸变校正 (5)

2.2.2图像去噪 (6)

2.3全景图像投影模型 (6)

2.4图像配准 (9)

2.5图像几何变换模型 (10)

2.6图像融合 (13)

2.6.1直接平均法 (13)

2.6.2加权平均法 (13)

2.6.3线性加权法 (13)

2.6.4多频段融合法 (14)

2.6.5泊松融合法 (15)

2.7本章小结 (15)

3基于重叠区域的改进SIFT图像配准算法 (16)

3.1几种常用的图像配准算法 (16)

3.1.1Harris算法 (16)

3.1.2SIFT算法 (17)

3.1.3SURF算法 (23)

3.2SIFT算法的性能分析 (26)

3.2.1鲁棒性分析 (27)

3.2.2算法速度分析 (32)

3.3特征点匹配和提纯 (33)

3.3.1特征粗匹配 (33)

西南科技大学硕士研究生学位论文IV

3.3.2单应性矩阵参数求解及RANSAC特征提纯 (33)

3.4基于重叠区域的改进SIFT算法 (36)

3.4.1重叠区域的估算 (36)

3.4.2分块特征匹配 (37)

3.5实验结果与分析 (39)

3.5.1改进SIFT算法的特征检测实验分析 (41)

3.5.2改进SIFT算法的特征匹配实验分析 (42)

3.6本章小结 (46)

4动态场景图像融合 (47)

4.1改进最佳缝合线算法 (47)

4.1.1基于动态规划的最佳缝合线搜索 (47)

4.1.2结合纹理差异因子的最佳缝合线搜索准则 (48)

4.2改进线性加权融合 (50)

4.2.1亮度差异校正 (50)

4.2.2分段线性加权融合算法 (51)

4.3图像融合质量客观评价 (53)

4.4实验结果与分析 (53)

4.4.1亮度调整 (54)

4.4.2最佳缝合线搜索实验分析 (55)

4.4.3图像融合实验分析及质量评价 (58)

4.5本章小结 (60)

5柱面全景图像拼接的研究与实现 (61)

5.1柱面全景图像拼接整体流程 (61)

5.1.1图像序列的自动识别和排序 (62)

5.1.2柱面全景图像的生成方式 (63)

5.2柱面全景图像拼接实现结果及分析 (63)

5.2.1偏移校正 (65)

5.2.2曝光校正 (66)

5.2.3图像拼接质量评价 (67)

5.3交互式全景图像查看 (68)

5.4本章小结 (68)

总结与展望 (69)

全文总结 (69)

西南科技大学硕士研究生学位论文V

不足与展望 (69)

致谢 (71)

参考文献 (72)

攻读硕士学位期间发表的学术论文及研究成果 (76)