Document Detail


Learning a Confidence Measure for Optical Flow.
MedLine Citation:
PMID:  22868652     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We present a supervised learning based method to estimate a per-pixel confidence for optical flow vectors. Regions of low texture and pixels close to occlusion boundaries are known to be difficult for optical flow algorithms. Using a spatiotemporal feature vector, we estimate if a flow algorithm is likely to fail in a given region. Our method is not restricted to any specific class of flow algorithm, and does not make any scene specific assumptions. By automatically learning this confidence we can combine the output of several computed flow fields from different algorithms to select the best performing algorithm per pixel. Our optical flow confidence measure allows one to achieve better overall results by discarding the most troublesome pixels. We illustrate the effectiveness of our method on four different optical flow algorithms over a variety of real and synthetic sequences. For algorithm selection, we achieve the top overall results on a large test set, and at times even surpasses the results of the best algorithm among the candidates.
Authors:
Oisin Mac Aodha; Ahmad Humayun; Marc Pollefeys; Gabriel J Brostow
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-8-2
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  -     ISSN:  1939-3539     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2012 Aug 
Date Detail:
Created Date:  2012-8-7     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
University College London, London.
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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