Estimating the Structure of Textured Surfaces Using Local Affine Flow

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

This paper describes a novel approach for recovering the structure and motion of a rigid textured surface from an image sequence. Camera focal length is also recovered, yielding metric estimates of the structure without the need for pre-calibration. The key innovation is the use of local \em affine flow parameters as the measurements within an extended Kalman filter (EKF) estimation framework, in contrast to feature correspondences or optical flow used in previous approaches. This enables surface normals to be recovered in addition to depth, unlike a feature correspondence scheme, but without the computational limitation of an optical flow approach. The method is based on equating the affine parameters to a local linearisation of the 2-D motion field and using the EKF to provide recursive estimates of the 3-D structure and motion. Experiments on both synthetic and real sequences demonstrate that the approach has considerable potential.
Translated title of the contributionEstimating the Structure of Textured Surfaces Using Local Affine Flow
Original languageEnglish
Pages (from-to)92-101
JournalBritish Machine Vision Conference
Publication statusPublished - 2000

Bibliographical note

ISBN: 1901725138
Publisher: BMVA
Name and Venue of Conference: British Machine Vision Conference
Other identifier: 1000476

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