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Proceedings Paper

Graph-based multiple panorama extraction from unordered image sets
Author(s): Alexander Sibiryakov; Miroslaw Bober
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Paper Abstract

This paper presents a multi-image registration method, which aims at recognizing and extracting multiple panoramas from an unordered set of images without user input. A method for panorama recognition introduced by Lowe and Brown is based on extraction of a full set of scale invariant image features and fast matching in feature space, followed by post-processing procedures. We propose a different approach, where the full set of descriptors is not required, and a small number of them are used to register a pair of images. We propose feature point indexing based on corner strength value. By matching descriptor pairs with similar corner strengths we update clusters in rotation-scale accumulators, and a probabilistic approach determines when these clusters are further processed with RANSAC to find inliers of image homography. If the number of inliers and global similarity between images are sufficient, a fast geometry-guided point matching is performed to improve the accuracy of registration. A global registration graph, whose node weights are proportional to the image similarity in the area of overlap, is updated with each new registration. This allows the prediction of undiscovered image registrations by finding the shortest paths and corresponding transformation chains. We demonstrate our approach using typical image collections containing multiple panoramic sequences.

Paper Details

Date Published: 28 February 2007
PDF: 13 pages
Proc. SPIE 6498, Computational Imaging V, 649809 (28 February 2007); doi: 10.1117/12.704025
Show Author Affiliations
Alexander Sibiryakov, Mitsubishi Electric ITE B.V. (United Kingdom)
Miroslaw Bober, Mitsubishi Electric ITE B.V. (United Kingdom)

Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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