Robust parameterization and computation of the trifocal tensor

Conference item

This paper presents all algorithm for computing a maximum likelihood estimate (MLE) of the trifocal tensor. The input to the algorithm is three images of the same scene, and the output is the estimated tensor and corner and line feature matches across the three images that are consistent with this estimate.
Particular novelties of the algorithm are the computation of a trifocal tensor from six point correspondences, and a parameterization of the trifocal tensor which enforces the constraints between the tensor elements. The algorithm uses techniques from robust statistics and is fully automatic.
Results are presented for synthetic and real image triplets. The proposed parameterization is compared to other existing methods.

Authors: Torr, PHS; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Image and Vision Computing
• Volume: 15
• Issue: 8
• Pages: 591-605
• Publication date: 1998-05-19
• Acceptance date: 1996-06-10
• Event title: 7th British Machine Vision Conference 1996 (BMVC 1996)
• Event location: Edinburgh, UK
• Event website: https://www.bmva.org/bmvc/1996/index.html
• Event start date: 1996-09-09
• Event end date: 1996-09-12
• DOI: 10.1016/s0262-8856(97)00010-3
• EISSN: 1872-8138
• ISSN: 0262-8856
• Language: English
• Keywords: matching; trifocal tensor; robust estimation; maximum likelihood estimation