Using Conic Correspondences in Two Images to Estimate the Epipolar Geometry
(1998) IEEE International Conference on Computer Vision, 1998 In Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) p.761766 Abstract
 In this paper it is shown hour corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. The corresponding conics can, be images of either planar celtics or silhouettes of quadrics. It is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. Furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. Finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. A method to calculate... (More)
 In this paper it is shown hour corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. The corresponding conics can, be images of either planar celtics or silhouettes of quadrics. It is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. Furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. Finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. A method to calculate these solutions is also given together with an experimental validation (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/787366
 author
 Kahl, Fredrik ^{LU} and Heyden, Anders ^{LU}
 organization
 publishing date
 1998
 type
 Chapter in Book/Report/Conference proceeding
 publication status
 published
 subject
 keywords
 computational geometry, computer vision, motion estimation, conic correspondences, epipolar geometry, image estimation, planar celtics, silhouettes, quadrics, fundamental matrix
 in
 Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)
 pages
 761  766
 conference name
 IEEE International Conference on Computer Vision, 1998
 external identifiers

 Scopus:0032309499
 ISBN
 81 7319 221 9
 DOI
 10.1109/ICCV.1998.710803
 language
 English
 LU publication?
 yes
 id
 9660bbf5de1e46839760ca06520c5212 (old id 787366)
 date added to LUP
 20080915 16:23:48
 date last changed
 20160416 12:18:27
@misc{9660bbf5de1e46839760ca06520c5212, abstract = {In this paper it is shown hour corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. The corresponding conics can, be images of either planar celtics or silhouettes of quadrics. It is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. Furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. Finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. A method to calculate these solutions is also given together with an experimental validation}, author = {Kahl, Fredrik and Heyden, Anders}, isbn = {81 7319 221 9}, keyword = {computational geometry,computer vision,motion estimation,conic correspondences,epipolar geometry,image estimation,planar celtics,silhouettes,quadrics,fundamental matrix}, language = {eng}, pages = {761766}, series = {Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)}, title = {Using Conic Correspondences in Two Images to Estimate the Epipolar Geometry}, url = {http://dx.doi.org/10.1109/ICCV.1998.710803}, year = {1998}, }