一种新的宽基线图像匹配方法

doi:10.3969/j.issn.1001-2400.2011.02.021

J4 ›› 2011, Vol. 38 ›› Issue (2): 116-123.doi: 10.3969/j.issn.1001-2400.2011.02.021

一种新的宽基线图像匹配方法

陈冰;赵亦工;李欣

(西安电子科技大学模式识别与智能控制研究所，陕西西安 710071)

收稿日期:2010-01-24 出版日期:2011-04-20 发布日期:2011-05-26
通讯作者: 陈冰
作者简介:陈冰(1984-)，男，西安电子科技大学博士研究生，E-mail: ice32bit@yahoo.cn．

Wide baseline image matching based on a new local descriptor

CHEN Bing;ZHAO Yigong;LI Xin

(Inst. of Pattern Recognition and Intelligent Control, Xidian Univ., Xi'an 710071， China)

Received:2010-01-24 Online:2011-04-20 Published:2011-05-26
Contact: CHEN Bing

摘要/Abstract

摘要：

在宽基线图像匹配中，图像存在3维视角、尺度、旋转和灰度差异．为此，构造了一种新的基于局部二值模式直方图傅里叶特征的特征描述符，并通过对传统宽基线图像匹配算法框架中不同部分算法的对比分析，提出了一种新的宽基线图像匹配方法．首先，提取基准图像和实时图像中具有尺度和仿射不变性的最稳定极值区域，并利用新的特征描述符对这些区域进行图像旋转和灰度不变性描述；然后，根据近邻欧氏距离比值准则提取两图像中匹配的最稳定极值特征区域对；最后，利用顺序抽样一致性算法剔除误匹配特征区域对，估计两图像的外极几何关系，得到匹配结果．仿真结果表明，新算法能够适应待匹配图像间较大的3维视角、尺度、旋转和灰度差异，实现稳定的宽基线图像匹配．

关键词: 机器视觉, 宽基线图像匹配, 最稳定极值区域, 局部二值模式直方图傅里叶特征, 特征匹配, 外极几何关系

Abstract:

In order to overcome the differences in 3D viewpoint, scale, rotation and grayscale, and achieve stable wide baseline image matching, a new feature descriptor based on Local Binary Pattern Histogram Fourier features is constructed and a new wide baseline image matching approach is proposed according to comparison and analysis. First, Maximally Stable Extremal Regions (MSER) of the reference image and real-time image which are scale and affine invariant are extracted, respectively. Second, rotation and grayscale invariant descriptors are constructed. Then the matching MSER features of the two images are extracted based on the nearest neighbor Euclid distance ratio strategy. The epipolar geometry of the two images is estimated according to the Progressive Sample Consensus (PROSAC) algorithm. Simulation results show that the proposed method is robust to changes in 3D viewpoints, scale, rotation and grayscale, and can achieve stable wide baseline image matching.

Key words: machine vision, wide baseline image matching, maximally stable extremal regions, local binary pattern histogram Fourier features, feature matching, epipolar geometry

陈冰;赵亦工;李欣. 一种新的宽基线图像匹配方法[J]. J4, 2011, 38(2): 116-123.

CHEN Bing;ZHAO Yigong;LI Xin. Wide baseline image matching based on a new local descriptor[J]. J4, 2011, 38(2): 116-123.

参考文献

［1］ Loncomill A P, Ruiz-Del-Sola J. Robust Object Recognition Using Wide Baseline Matching for RoboCup Application ［C］//RoboCup 2007: Robot Soccer World Cup XI. Berlin: Springer-Verlag, 2008: 441-448.
［2］ Goedemt T, Tuytelaars T, Gool L V. Fast Wide Baseline Matching for Visual Navigation ［C］//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 1. Washington: IEEE Computer Society, 2004: 24-29.
［3］ Brunelli R. Template Matching Technique in Computer Vision: Theory and Practice ［M］. West Sussex: John Wiley ＆ Sons Ltd, 2009: 181-199.
［4］ Schmid C, Mohr R. Local Grayvalue Invariants for Image Retrieval ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(5): 530-535.
［5］ Mikolajczyk K, Schmid C. Scale ＆ Affine Invariant Interest Point Detectors ［J］. International Journal of Computer Vision, 2004, 60(1):63-86.
［6］ Tuytelaars T, Gool L V. Matching Widely Separated Views Based on Affine Invariant Regions ［J］. International Journal of Computer Vision, 2004, 59(1):61-85.
［7］ Kadir T, Zisserman A, Brady M. An Affine Invariant Salient Regions Detector ［C］//Proceedings of the 8th European Conference on Computer Vision. Prague: Springer-Verlag, 2004: 228-241.
［8］ Matas J, Chum O, Urban M, et al. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions ［C］//Proceedings of the British Machine Vision Conference. Cardiff: British Machine Vision Association, 2002: 384-393.
［9］ Lowe D. Distinctive Image Features from Scale-invariant Keypoints ［J］. International Journal of Computer Vision, 2004, 60(2): 91-110.
［10］ Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(10):1615-1630.
［11］ Bay H, Tuytelaars T, Gool L V. SURF: Speeded Up Robust Features ［C］//European Conference on Computer Vision: 1. Graz: Springer-Verlag, 2006: 404-417.
［12］ Ahonen T, Matas J, He C, et al. Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features ［C］//Image Analysis, SCIA 2009 Proceedings, Lecture Notes in Computer Science: 5575. Berlin: Springer-Verlag, 2009: 61-70.
［13］ Ojala T, Pietikainen M, Maenpaa T. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.
［14］ Vincent L, Soille P. Watersheds in Digital Spaces: an Efficient Algorithm Based on Immersion Simulations ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
［15］ Murphy-Chutorian E, Triyedi M. N-tree Disjoint-set Forests for Maximally Stable Extremal Regions ［C］//Proceedings of the British Machine Vision Conference 2. Edinburgh: BMVC, 2006: 739-748.
［16］ Mikolajczyk K, Tuytelaars T, Schmid C, et al. A Comparison of Affine Region Detectors ［J］. International Journal of Computer Vision, 2005, 65(1/2):43-72.
［17］ Heikkila M, Pietikainen M, Schmid C. Description of Interest Regions with Local Binary Patterns ［J］. Pattern Recognition, 2009, 42(3):425-436.
［18］ Harley R, Zisserman A. Multiple View Geometry in Computer Vision ［M］. 2^nd Edition. Cambridge: Cambridge University Press, 2003:290-293.
［19］ Chum O, Matas J. Matching with PROSAC-Progressive Sample Consensus ［C］//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 1. Washington: IEEE Computer Society, 2005: 220-226.

[1]	王璐璐,张为,孙琦龙. 一种改进RetinaNet的室内人员检测算法[J]. 西安电子科技大学学报, 2019, 46(5): 69-74.
[2]	罗楠;王泉. 点云配准中初始变换的快速优化求解算法[J]. 西安电子科技大学学报, 2017, 44(5): 69-74.
[3]	何周灿;王庆;杨恒. 图像特征匹配中一种快速关键维过滤搜索算法[J]. J4, 2010, 37(3): 534-540.

一种新的宽基线图像匹配方法

Wide baseline image matching based on a new local descriptor

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

Metrics

本文评价

推荐阅读 0