Optimization of Endoscopic Video Stabilization by Local Motion Exclusion

Thomas Gross, Navya Amin, Marvin C. Offiah, Susanne Rosenthal, Nail El-Sourani, Markus Borschbach

2014

Abstract

Hitherto video stabilization algorithms for different types of videos have been proposed. Our work majorly focuses on developing stabilization algorithms for endoscopic videos which include distortions peculiar to endoscopy. In this paper, we deal with the optimization of the motion detection procedure which is the most important step in the development of a video stabilization algorithm. It presents a robust motion estimation procedure to enhance the quality of the outcome. The outcome of the later steps in the stabilization, namely motion compensation and image composition depend on the level of precision of the motion estimation step. The results of a previous version of the stabilization algorithm are here compared to a new optimized version. Furthermore, the improvements of the outcomes using the video quality estimation methods are also discussed.

References

  1. Amin, N., Gross, T., Offiah, M. C., Rosenthal, S., ElSourani, N., and Borschbach, M. (2014). Stabilization of endoscopic videos using camera path from global motion vectors. In To appear in the 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
  2. Berna, E. and Faouzi, K. (2000). Partitioning of video objects into temporal segments using local motion information. In ICIP, pages 945-948.
  3. Bradski1, G. R. and Davis2, J. W. (2002). Motion segmentation and pose recognition with motion history gradients. In Machine Vision and Applications.
  4. C.Offiah, M., Amin, N., Gross, T., El-Sourani, N., and Borschbach, M. (2012). Towards a benchmarking framework for quality-optimized endoscopic video stabilization. In ELMAR, Sep.,2012 Proceedings, pages 23-26.
  5. Flores-Mangas, F. and Jepson, A. D. (2013). Fast rigid motion segmentation via incrementally-complex local models. In IEEE Conference on Computer Vision and Pattern Recognition.
  6. Frey, A. (2012). Research center borstel - leibniz center for medicine and biosciences, germany. http://www.fzborstel.de/cms/index.php.
  7. Georgia, A. M., Alexander, G., Andreas, K., Thomas, S., Marta, M., and M., K. A. (2009). Global motion estimation using variable block sizes and its application to object segmentation. In WIAMIS, pages 173-176.
  8. Grundmann, M., Kwatra, V., and Essa, I. (2011). Autodirected video stabilization with robust l1 optimal camera paths. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  9. Han, M., Xu, W., and Gong, Y. (2006). Video foreground segmentation based on sequential feature clustering. In 18th International Conference on Pattern Recognition, ICPR 2006., volume 1, pages 492-496.
  10. HTWK Leipzig (2013). http://www.htwk-leipzig.de.
  11. Morimotoa, C. and Chellappa, R. (1998). Evaluation of image stabilization algorithms. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 5:2789-2792.
  12. Offiah, M. C., Amin, N., Gross, T., El-Sourani, N., and Borschbach, M. (2012). On the ability of state-ofthe-art tools to stabilize medical endoscopic video sequences. In MedImage 2012, Mumbai.
  13. Shafie, A. A., Hafiz, F., and Ali, M. H. (2009). Motion detection techniques using optical flow. In World Academy of Science, Engineering and Technology.
  14. Shi, J. and Tomasi, C. (1994). Good features to track. In IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600.
  15. Tomasi, C. and Kanade, T. (1991). Detection and tracking of point features. In Carnegie Mellon University Technical Report CMU-CS-91-132.
  16. Walker, J. T. (1999). Statistics in Criminal Justice: Analysis and Interpretation. Aspen Puplishers Inc.
  17. Zhao, F., Wang, H., Chai, X., and Ge, S. (2009). A fast and effective outlier detection method for matching uncalibrated images. In ICIP, pages 2097-2100.
  18. Zhou, Z., Jin, H., and Ma, Y. (2013). Content-preserving warps for 3d video stabilization. In IEEE Conference on Computer Vision and Pattern Recognition.
Download


Paper Citation


in Harvard Style

Gross T., Amin N., C. Offiah M., Rosenthal S., El-Sourani N. and Borschbach M. (2014). Optimization of Endoscopic Video Stabilization by Local Motion Exclusion . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 64-72. DOI: 10.5220/0004745900640072


in Bibtex Style

@conference{visapp14,
author={Thomas Gross and Navya Amin and Marvin C. Offiah and Susanne Rosenthal and Nail El-Sourani and Markus Borschbach},
title={Optimization of Endoscopic Video Stabilization by Local Motion Exclusion},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={64-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004745900640072},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Optimization of Endoscopic Video Stabilization by Local Motion Exclusion
SN - 978-989-758-009-3
AU - Gross T.
AU - Amin N.
AU - C. Offiah M.
AU - Rosenthal S.
AU - El-Sourani N.
AU - Borschbach M.
PY - 2014
SP - 64
EP - 72
DO - 10.5220/0004745900640072