TY - GEN
T1 - Robust motion estimation and structure recovery from endoscopic image sequences with an adaptive scale Kernel Consensus estimator
AU - Wang, Hanzi
AU - Mirota, Daniel
AU - Ishii, Masaru
AU - Hager, Gregory D.
PY - 2008
Y1 - 2008
N2 - To 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.
AB - To 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.
UR - http://www.scopus.com/inward/record.url?scp=52249086497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=52249086497&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2008.4587687
DO - 10.1109/CVPR.2008.4587687
M3 - Conference contribution
C2 - 20607128
AN - SCOPUS:52249086497
SN - 9781424422432
T3 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
BT - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Y2 - 23 June 2008 through 28 June 2008
ER -