Robust motion estimation and structure recovery from endoscopic image sequences with an Adaptive Scale Kernel Consensus estimator

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 mat...

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Bibliographic Details
Published in:2008 IEEE Conference on Computer Vision and Pattern Recognition Vol. 2008; pp. 1 - 7
Main Authors: Wang, Hanzi, Mirota, Daniel, Ishii, Masaru, Hager, Gregory D.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 23.06.2008
Subjects:
Anatomy
Biomedical imaging
Cameras
Image reconstruction
Image sequences
Kernel
Motion estimation
Robustness
Statistics
Surgery
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary: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.
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ISBN:9781424422425
1424422426
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2008.4587687