Open AccessLocal similarity refinement of shape‐preserved warping for parallax‐tolerant image stitching
Author(s) -
Li Wei,
Jin ChengBin,
Liu Mingjie,
Kim Hakil,
Cui Xuenan
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0037
Subject(s) - image stitching , parallax , image warping , artificial intelligence , homography , computer vision , computer science , distortion (music) , similarity (geometry) , pattern recognition (psychology) , image (mathematics) , mathematics , projective test , projective space , amplifier , computer network , statistics , bandwidth (computing)
This study proposes a local similarity refinement strategy to handle the parallax problem in image stitching. The proposed method is combined with deconvolution to acquire high‐accuracy matching between corresponding source images. Shape‐preserving half‐projective warp was used to eliminate distortion across the non‐overlapping region caused by the global projective transformation. The proposed refinement method further refines the warping result within the overlapping region, where it suppresses the parallax. The method was compared with various state‐of‐the‐art methods: projective (global homography), AutoStitch, Zaragoza's method, Zhang's method, and Chang's approach. All comparisons are based on both public data sets and a proposed Inha University Computer Vision Lab (ICVL) stitching data set. The experimental results demonstrate that the proposed method is robust for handling the parallax in image stitching.