Ultra-wide Baseline Aerial Imagery Matching in Urban Environments

Correspondence matching is a core problem in computer vision. Under narrow baseline viewing conditions, this problem has been successfully addressed using SIFT-like approaches. However, under wide baseline viewing conditions these methods often fail. In this paper we propose a method for correspondence estimation that addresses this challenge for aerial scenes in urban environments. Our method creates synthetic views and leverages self-similarity cues to recover correspondences using a RANSAC-based approach aided by self-similarity graph-based sampling. We evaluate our method on 30 challenging image pairs and demonstrate improved performance to alternative methods in the literature.

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

[2]  Xinhua Zhuang,et al.  Pose estimation from corresponding point data , 1989, IEEE Trans. Syst. Man Cybern..

[3]  B. S. Manjunath,et al.  The multiRANSAC algorithm and its application to detect planar homographies , 2005, IEEE International Conference on Image Processing 2005.

[4]  Tony X. Han,et al.  Building recognition using sketch-based representations and spectral graph matching , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[6]  Siamak Khorram,et al.  A feature-based image registration algorithm using improved chain-code representation combined with invariant moments , 1999, IEEE Trans. Geosci. Remote. Sens..

[7]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Alexei A. Efros,et al.  Discovering Texture Regularity as a Higher-Order Correspondence Problem , 2006, ECCV.

[9]  In-So Kweon,et al.  Robust feature point matching by preserving local geometric consistency , 2009, Comput. Vis. Image Underst..

[10]  Andrew Zisserman,et al.  Wide baseline stereo matching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  Alexei A. Efros,et al.  Unsupervised Discovery of Mid-Level Discriminative Patches , 2012, ECCV.

[12]  Jean-Michel Morel,et al.  ASIFT: An Algorithm for Fully Affine Invariant Comparison , 2011, Image Process. Line.

[13]  Wei Zhang,et al.  Generalized RANSAC Framework for Relaxed Correspondence Problems , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[14]  Rahul Sukthankar,et al.  D-Nets: Beyond patch-based image descriptors , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[17]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[18]  Laveen N. Kanal,et al.  Recognition of spatial point patterns , 1983, Pattern Recognit..

[19]  Stefan Carlsson,et al.  Combining Appearance and Topology for Wide Baseline Matching , 2002, ECCV.

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

[21]  Edwin R. Hancock,et al.  Point pattern matching with robust spectral correspondence , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Mayank Bansal,et al.  Ultra-wide Baseline Facade Matching for Geo-localization , 2012, ECCV Workshops.

[23]  É. Vincent,et al.  Detecting planar homographies in an image pair , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[24]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[25]  Vincent Lepetit,et al.  Randomized trees for real-time keypoint recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[27]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[28]  Eli Shechtman,et al.  Matching Local Self-Similarities across Images and Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Kidiyo Kpalma,et al.  An automatic image registration for applications in remote sensing , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[31]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[32]  Robert C. Bolles,et al.  Robust Feature Matching Through Maximal Cliques , 1979, Other Conferences.

[33]  Tsuhan Chen,et al.  Image retrieval with geometry-preserving visual phrases , 2011, CVPR 2011.

[34]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[35]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .