Image stitching by feature positioning and seam elimination

Qin, Y; Li, J; Jiang, P; Jiang, F

Image stitching by feature positioning and seam elimination

Qin, Y Li, J Jiang, P Jiang, F

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2021, 80, (14), pp. 20869-20881
Issue Date:: 2021-06-01

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Field	Value	Language
dc.contributor.author	Qin, Y
dc.contributor.author	Li, J
dc.contributor.author	Jiang, P
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.date.accessioned	2022-01-04T04:06:45Z
dc.date.available	2022-01-04T04:06:45Z
dc.date.issued	2021-06-01
dc.identifier.citation	Multimedia Tools and Applications, 2021, 80, (14), pp. 20869-20881
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/152657
dc.description.abstract	Parallax, exposure differences, ghost and efficiency handling are the challenging tasks for image stitching, which is regarded as the promising approach to resolve the issues in the tasks. In this paper, we propose a novel stitching method that locates the overlapped regions of the input images, and records the feature points at the same time. The warping of each image is then guided by a mesh interpolation map in a local warp model. We also propose an arc function weight model to eliminate image chromatic aberration. It is proved via the validation cases that our approach shows constantly the better performance than the AutoStitch, APAP, SPHP, ANAP, ELA and many other state-of-the-art methods. Our method can effectively avoid mismatched points, improve the matching efficiency of feature points of large-size images by about 60%, eliminate the color difference seam and ghost of the image, and still have good accuracy and stability in complex scenes.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-021-10694-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	Software Engineering
dc.title	Image stitching by feature positioning and seam elimination
dc.type	Journal Article
utslib.citation.volume	80
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-04T04:06:43Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	80
utslib.citation.issue	14

Abstract:

Parallax, exposure differences, ghost and efficiency handling are the challenging tasks for image stitching, which is regarded as the promising approach to resolve the issues in the tasks. In this paper, we propose a novel stitching method that locates the overlapped regions of the input images, and records the feature points at the same time. The warping of each image is then guided by a mesh interpolation map in a local warp model. We also propose an arc function weight model to eliminate image chromatic aberration. It is proved via the validation cases that our approach shows constantly the better performance than the AutoStitch, APAP, SPHP, ANAP, ELA and many other state-of-the-art methods. Our method can effectively avoid mismatched points, improve the matching efficiency of feature points of large-size images by about 60%, eliminate the color difference seam and ghost of the image, and still have good accuracy and stability in complex scenes.

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http://hdl.handle.net/10453/152657