Homography estimation from correspondences of local elliptical features

We propose a novel unified approach for homography estimation from two or more correspondences of local elliptical features. The method finds a homography defined by first-order Taylor expansions at two (or more) points. The approximations are affine transformations that are constrained by the ellipse-to-ellipse correspondences. Unlike methods based on projective invariants of conics, the proposed method generates only a single homography model per pair of ellipse correspondences. We show experimentally, that the proposed method generates models of precision comparable or better than the state-of-the-art at lower computational costs.

[1]  Jiri Matas,et al.  Locally Optimized RANSAC , 2003, DAGM-Symposium.

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

[3]  Christos Conomis Conics-Based Homography Estimation from Invariant Points and Pole-Polar Relationships , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[4]  Long Quan,et al.  Conic Reconstruction and Correspondence From Two Views , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[6]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[7]  David A. Forsyth,et al.  Relative motion and pose from arbitrary plane curves , 1992, Image Vis. Comput..

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

[9]  Anders Heyden,et al.  Using conic correspondences in two images to estimate the epipolar geometry , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

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

[12]  Tomás Pajdla,et al.  The geometric error for homographies , 2003, Comput. Vis. Image Underst..

[13]  Andrew Zisserman,et al.  Efficient image retrieval for 3D structures , 2010, BMVC.

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

[15]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Juho Kannala,et al.  Algorithms for Computing a Planar Homography from Conics in Correspondence , 2006, BMVC.

[17]  Akihiro Sugimoto A Linear Algorithm for Computing the Homography from Conics in Correspondence , 2004, Journal of Mathematical Imaging and Vision.

[18]  Amnon Shashua,et al.  Multiple View Geometry of General Algebraic Curves , 2004, International Journal of Computer Vision.

[19]  David A. Forsyth,et al.  Invariant Descriptors for 3D Object Recognition and Pose , 1991, IEEE Trans. Pattern Anal. Mach. Intell..