Fast stitching algorithm for moving object detection and mosaic construction

Jun-Wei Hsieh*

*Corresponding author for this work

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

6 Scopus citations

Abstract

This paper proposes a novel edge-based stitching method to detect moving objects and construct mosaics from images. The method is a coarse-to-fine scheme which first estimates a good initialization of camera parameters with two complementary methods and then refines the solution through an optimization process. The two complementary methods are the edge alignment and correspondence-based approaches, respectively. Since these two methods are complementary to each other, the desired initial estimate can be obtained more robustly. After that, a Monte-Carlo style method is then proposed for integrating these two methods together. Then, an optimization process is applied to refine the above initial parameters. Since the found initialization is very close to the exact solution and only errors on feature positions are considered for minimization, the optimization process can be very quickly achieved. Experimental results are provided to verify the superiority of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2003 International Conference on Multimedia and Expo, ICME
PublisherIEEE Computer Society
PagesI85-I88
ISBN (Electronic)0780379659
DOIs
StatePublished - 1 Jan 2003
Event2003 International Conference on Multimedia and Expo, ICME 2003 - Baltimore, United States
Duration: 6 Jul 20039 Jul 2003

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume1
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2003 International Conference on Multimedia and Expo, ICME 2003
CountryUnited States
CityBaltimore
Period6/07/039/07/03

Fingerprint Dive into the research topics of 'Fast stitching algorithm for moving object detection and mosaic construction'. Together they form a unique fingerprint.

Cite this