Please use this identifier to cite or link to this item:
Title: When discrete meets differential : AAAssessing the stability of structure from small motion
Authors: Lin, W.-Y.
Tan, G.-C.
Cheong, L.-F. 
Keywords: Perturbation analysis
Structure from motion
Issue Date: Jan-2010
Citation: Lin, W.-Y., Tan, G.-C., Cheong, L.-F. (2010-01). When discrete meets differential : AAAssessing the stability of structure from small motion. International Journal of Computer Vision 86 (1) : 87-110. ScholarBank@NUS Repository.
Abstract: We provide a theoretical proof showing that under a proportional noise model, the discrete eight point algorithm behaves similarly to the differential eight point algorithm when the motion is small. This implies that the discrete algorithm can handle arbitrarily small motion for a general scene, as long as the noise decreases proportionally with the amount of image motion and the proportionality constant is small enough. This stability result extends to all normalized variants of the eight point algorithm. Using simulations, we show that given arbitrarily small motions and proportional noise regime, the normalized eight point algorithms outperform their differential counterparts by a large margin. Using real data, we show that in practical small motion problems involving optical flow, these discrete structure from motion (SFM) algorithms also provide better estimates than their differential counterparts, even when the motion magnitudes reach sub-pixel level. The better performance of these normalized discrete variants means that there is much to recommend them as differential SFM algorithms that are linear and normalized. © 2009 Springer Science+Business Media, LLC.
Source Title: International Journal of Computer Vision
ISSN: 09205691
DOI: 10.1007/s11263-009-0260-y
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Feb 20, 2020


checked on Feb 20, 2020

Page view(s)

checked on Feb 18, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.