Seamless image stitching in the gradient domain

Anat Levin*, Assaf Zomet, Shmuel Peleg, Yair Weiss

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

349 Scopus citations

Abstract

Image stitching is used to combine several individual images having some overlap into a composite image. The quality of image stitching is measured by the similarity of the stitched image to each of the input images, and by the visibility of the seam between the stitched images. In order to define and get the best possible stitching, we introduce several formal cost functions for the evaluation of the quality of stitching. In these cost functions, the similarity to the input images and the visibility of the seam are defined in the gradient domain, minimizing the disturbing edges along the seam. A good image stitching will optimize these cost functions, overcoming both photometric inconsistencies and geometric misalignments between the stitched images. This approach is demonstrated in the generation of panoramic images and in object blending. Comparisons with existing methods show the benefits of optimizing the measures in the gradient domain.

Original languageAmerican English
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsTomas Pajdla, Jiri Matas
PublisherSpringer Verlag
Pages377-389
Number of pages13
ISBN (Print)3540219811
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3024
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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