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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 157847, 6 pages
Research Article

A Fast Image Stitching Algorithm via Multiple-Constraint Corner Matching

1College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350108, China
2College of Civil Engineering, Fuzhou University, Fuzhou, Fujian 350108, China
3School of Computing, University of South Alabama, Mobile, AL 36688, USA

Received 8 September 2013; Accepted 30 September 2013

Academic Editor: Artde Donald Kin-Tak Lam

Copyright © 2013 Minchen Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Video panoramic image stitching is in general challenging because there is small overlapping between original images, and stitching processes are therefore extremely time consuming. We present a new algorithm in this paper. Our contribution can be summarized as a multiple-constraint corner matching process and the resultant faster image stitching. The traditional Random Sample Consensus (RANSAC) algorithm is inefficient, especially when stitching a large number of images and when these images have quite similar features. We first filter out many inappropriate corners according to their position information. An initial set of candidate matching-corner pairs is then generated based on grayscales of adjacent regions around each corner. Finally we apply multiple constraints, e.g., their midpoints, distances, and slopes, on every two candidate pairs to remove incorrectly matched pairs. Consequently, we are able to significantly reduce the number of iterations needed in RANSAC algorithm so that the panorama stitching can be performed in a much more efficient manner. Experimental results demonstrate that (i) our corner matching is three times faster than normalized cross-correlation function (NCC) rough match in traditional RANSAC algorithm and (ii) panoramas generated from our algorithm feature a smooth transition in overlapping image areas and satisfy human visual requirements.