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dc.contributor.authorWang, H.en
dc.contributor.authorMirota, D.en
dc.contributor.authorIshii, M.en
dc.contributor.authorHager, G.en
dc.identifier.citationIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2008: pp.1-7en
dc.description.abstractTo correctly estimate the camera motion parameters and reconstruct the structure of the surrounding tissues from endoscopic image sequences, we need not only to deal with outliers (e.g., mismatches), which may involve more than 50% of the data, but also to accurately distinguish inliers (correct matches) from outliers. In this paper, we propose a new robust estimator, Adaptive Scale Kernel Consensus (ASKC), which can tolerate more than 50 percent outliers while automatically estimating the scale of inliers. With ASKC, we develop a reliable feature tracking algorithm. This, in turn, allows us to develop a complete system for estimating endoscopic camera motion and reconstructing anatomical structures from endoscopic image sequences. Preliminary experiments on endoscopic sinus imagery have achieved promising results.en
dc.description.statementofresponsibilityHanzi Wang; Mirota, D.; Ishii, M. and Hager, G.D.en
dc.titleRobust motion estimation and structure recovery from endoscopic image sequences with an adaptive Scale Kernel Consensus estimatoren
dc.typeConference paperen
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (21st : 2008 : Anchorage, AK)en
pubs.library.collectionComputer Science publicationsen
Appears in Collections:Computer Science publications

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