Robust Motion Estimation and Structure Recovery from Endoscopic Image Sequences with an Adaptive Scale Kernel Consensus Estimator
Abstract
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.
Cite
Text
Wang et al. "Robust Motion Estimation and Structure Recovery from Endoscopic Image Sequences with an Adaptive Scale Kernel Consensus Estimator." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587687Markdown
[Wang et al. "Robust Motion Estimation and Structure Recovery from Endoscopic Image Sequences with an Adaptive Scale Kernel Consensus Estimator." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/wang2008cvpr-robust/) doi:10.1109/CVPR.2008.4587687BibTeX
@inproceedings{wang2008cvpr-robust,
title = {{Robust Motion Estimation and Structure Recovery from Endoscopic Image Sequences with an Adaptive Scale Kernel Consensus Estimator}},
author = {Wang, Hanzi and Mirota, Daniel and Ishii, Masaru and Hager, Gregory D.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587687},
url = {https://mlanthology.org/cvpr/2008/wang2008cvpr-robust/}
}