Closed-form solution + maximum likelihood: A robust approach to motion and structure estimation.

Juyang Weng, Narendra Ahuja, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A robust approach is presented to estimating motion and structure from image sequences. The approach consists of two steps. The first step is estimating the motion parameters using a robust linear algorithm that gives a closed-form solution for motion parameters and scene structure. The second step is improving the results from the linear algorithm using maximum-likelihood estimation. An algorithm using point correspondences from monocular images is discussed in detail and experimented with. An algorithm using line correspondences is briefly discussed. The simulations show that maximum-likelihood estimation achieves remarkable improvement over the preliminary estimates given by the linear algorithm. The algorithm is also tested on images of real scenes from automatically computed displacement field. The proposed approach is independent of the exact tokens used to establish correspondences, e.g. displacement flow, optical flow, or discrete features. Two or more types of tokens may be used, for monocular or binocular images.

Original languageEnglish (US)
Title of host publicationProc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit
PublisherPubl by IEEE
Pages381-386
Number of pages6
ISBN (Print)0818608625
StatePublished - Dec 1 1988

Publication series

NameProc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Closed-form solution + maximum likelihood: A robust approach to motion and structure estimation.'. Together they form a unique fingerprint.

  • Cite this

    Weng, J., Ahuja, N., & Huang, T. S. (1988). Closed-form solution + maximum likelihood: A robust approach to motion and structure estimation. In Proc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit (pp. 381-386). (Proc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit). Publ by IEEE.