DocumentCode
2178722
Title
Recognising panoramas
Author
Brown, M. ; Lowe, D.G.
Author_Institution
Dept. of Comput. Sci., British Columbia Univ., Vancouver, BC, Canada
fYear
2003
fDate
13-16 Oct. 2003
Firstpage
1218
Abstract
The problem considered in this paper is the fully automatic construction of panoramas. Fundamentally, this problem requires recognition, as we need to know which parts of the panorama join up. Previous approaches have used human input or restrictions on the image sequence for the matching step. In this work we use object recognition techniques based on invariant local features to select matching images, and a probabilistic model for verification. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the images. It is also insensitive to ´noise´ images which are not part of the panorama at all, that is, it recognises panoramas. This suggests a useful application for photographers: the system takes as input the images on an entire flash card or film, recognises images that form part of a panorama, and stitches them with no user input whatsoever.
Keywords
computer vision; feature extraction; image matching; image reconstruction; image sequences; object recognition; Leverberg-Marquardt algorithm; automatic panorama construction; automatic panorama stitching; camera matrix; digital cameras; feature extraction; feature matching; image illumination; image matching; image ordering; image orientation; image scale; image sequence; invariant local features; local intensity values; multiband blending; nonlinear least squares problem; normalised cross-correlation; object recognition techniques; panorama recognition; panoramic image geometry; panoramic image mosaicing; photographers; scale invariant feature transform; Computer vision;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on
Conference_Location
Nice, France
Print_ISBN
0-7695-1950-4
Type
conf
DOI
10.1109/ICCV.2003.1238630
Filename
1238630
Link To Document