A Triangulation-based Hierarchical Image Matching Method for Wide-Baseline Images

This paper presents a triangulation-based hierarchical image matching method for wide-baseline images. The method includes the following three steps: (a) image orientation by incorporating the SIFT algorithm with the RANSAC approach, (b) feature matching based on the self-adaptive triangle constraint, which includes point-to-point matching and subsequent point-to-area matching, and (c) triangulation constrained dense matching based on the previous matched results. Two new constraints, the triangulation-based disparity constraint and triangulation-based gradient orientation constraint, are developed to alleviate the matching ambiguity for wide-baseline images. A triangulation based affine-adaptive cross-correlation is developed to help find correct matches even in the image regions with large perspective distortions. Experiments using Mars ground wide-baseline images and terrestrial wide-baseline images revealed that the proposed method is capable of generating reliable and dense matching results for terrain mapping and surface reconstruction from the wide-baseline images.

[1]  U. Helava.  Digital correlation in photogrammetric instruments , 1978 .

[2]  J. Mayhew,et al.  Disparity Gradient, Lipschitz Continuity, and Computing Binocular Correspondences , 1985 .

[3]  A. Gruen.  ADAPTIVE LEAST SQUARES CORRELATION: A POWERFUL IMAGE MATCHING TECHNIQUE , 1985 .

[4]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[5]  G. P. Otto,et al.  A "Region-Growing" Algorithm for Matching of Terrain Images , 1988, Alvey Vision Conference.

[6]  G. P. Otto,et al.  "Region-growing" algorithm for matching of terrain images , 1989, Image Vis. Comput..

[7]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[9]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[10]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[11]  Long Quan,et al.  Match Propagation for Image-Based Modeling and Rendering , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Luc Van Gool,et al.  Dense matching of multiple wide-baseline views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Clark F. Olson,et al.  Wide-baseline stereo vision for Mars rovers , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[14]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[15]  K. Di,et al.  Rover Localization and Landing Site Mapping Technology for the 2003 Mars Exploration Rover Mission , 2004 .

[16]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[17]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[18]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[19]  Dmitry Chetverikov,et al.  Affine propagation for surface reconstruction in wide baseline stereo , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[20]  J. Gong,et al.  Triangulation of Well-Defined Points as a Constraint for Reliable Image Matching , 2005 .

[21]  C.F. Olson,et al.  Wide-baseline stereo experiments in natural terrain , 2005, ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005..

[22]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  David Nister,et al.  Recent developments on direct relative orientation , 2006 .

[24]  Bo Wu,et al.  A filtering strategy for interest point detecting to improve repeatability and information content , 2007 .

[25]  K. Di,et al.  TOPOGRAPHIC MAPPING CAPABILITY ANALYSIS OF MARS EXPLORATION ROVER 2003 MISSION IMAGERY , 2007 .

[26]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[27]  Larry H. Matthies,et al.  Rock modeling and matching for autonomous long‐range Mars rover localization , 2007, J. Field Robotics.

[28]  Juho Kannala,et al.  Quasi-Dense Wide Baseline Matching Using Match Propagation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Qing Zhu,et al.  Propagation strategies for stereo image matching based on the dynamic triangle constraint , 2007 .

[30]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Clark F. Olson,et al.  Wide-baseline stereo vision for terrain mapping , 2009, Machine Vision and Applications.

[32]  Feng Zhang,et al.  Quasi-dense Matching by Neighborhood Transfer for Fish-eye Images: Quasi-dense Matching by Neighborhood Transfer for Fish-eye Images , 2009 .

[33]  Yongjun Zhang,et al.  Photogrammetry for First Response in Wenchuan Earthquake , 2009 .

[34]  Davide Marenchino,et al.  Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications , 2009, Sensors.

[35]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.