Computing correspondence based on regions and invariants without feature extraction and segmentation

Chi Yin Lee, David B. Cooper, Daniel Keren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The problem addressed in this paper is matching corresponding regions into images, even when the image intensity may be smoothly varying without distinctive edges. Corresponding small regions are assumed to be related by affine transformations. The matching is done by using a new class of low computational cost affine invariants. This approach also computes the affine transformation and is ideal for applications to 3D motion estimation and 3D surface reconstruction, image alignment, etc.. No feature extraction, segmentation or epipolar constraint is required. The very important advantage of our approach over area matching is that it handles large baselines, i.e., distance between camera positions where the differences in orientation and linear distortion of two areas being compared is large.

Original languageEnglish
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages655-656
Number of pages2
ISBN (Print)0818638826
StatePublished - 1993
Externally publishedYes
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: 15 Jun 199318 Jun 1993

Publication series

NameIEEE Computer Vision and Pattern Recognition

Conference

ConferenceProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period15/06/9318/06/93

ASJC Scopus subject areas

  • Engineering (all)

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