MODEL BASED GLOBAL IMAGE REGISTRATION
Niloofar Gheissari, Mostafa Kamali, Parisa Mirshams, Zohreh Sharafi
2008
Abstract
In this paper, we propose a model-based image registration method capable of detecting the true transformation model between two images. We incorporate a statistical model selection criterion to choose the true underlying transformation model. Therefore, the proposed algorithm is robust to degeneracy as any degeneracy is detected by the model selection component. In addition, the algorithm is robust to noise and outliers since any corresponding pair that does not undergo the chosen model is rejected by a robust fitting method adapted from the literature. Another important contribution of this paper is evaluating a number of different model selection criteria for image registration task. We evaluated all different criteria based on different levels of noise. We conclude that CAIC and GBIC slightly outperform other criteria for this application. The next choices are GIC, SSD and MDL. Finally, we create panorama images using our registration algorithm. The panorama images show the success of this algorithm.
References
- Akaike, H. 1974. "A New Look at the Statistical Model Identification." IEEE Transactions on Automatic Control AC-19(6):716-723.
- Bab-Hadiashar, A. and D. Suter. 1999. "Robust Segmentation of Visual Data Using Ranked Unbiased Scale Estimate." ROBOTICA, International Journal of Information, Education and Research in Robotics and Artificial Intelligence 17:649-660.
- Bay, Herbert, Tinne Tuytelaars and Van Luc Gool. 2006. "SURF: Speeded Up Robust Features." In Proceedings of the 9th European Conference on Computer Vision (ECCV06).
- Bozdogan, H. 1987. "Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extentions." Psychometrica 52:345-370.
- Chickering, D. and D. Heckerman. 1997. "Efficient Approximation for the Marginal Likelihood of Bayesian Networks with Hidden Variables." Machine Learning 29(2-3):181-212.
- Gheissari, Niloofar, Bab-Hadiashar,Alireza. Dec. 2003. "Model Selection Criteria in Computer Vision: Are They Different?" In Proceedings of Digital Image Computing Techniques and Applications(DICTA 2003). Sydney,Australia.
- Kanatani, K. 2000. "Model Selection Criteria for Geometric Inference." In Data Segmentation and Model Selection for Computer Vision: A statistical Approach, ed. A. and Suter Bab-Hadiashar, D.: Springer-verlag.
- Kanatani, K. Jan. 2002. "Model Selection for Geometric Inference." In The 5th Asian Conference on Computer Vision. Melbourne, Australia.
- Lowe, David. 2004. "Distinctive image features from scale-invariant keypoints, cascade filtering approach." International Journal of Computer Vision 60:91 - 110.
- Mallows, C. L. 1973 "Some Comments on CP." Technometrics 15(4):661-675
- Rissanen, J. 1978. "Modeling by Shortest Data Description Automata." 14:465 - 471.
- Rissanen, J. 1984. "Universal Coding, Information, Prediction and Estimation." IEEE Transactions on Information Theory 30(4):629-636.
- Szeliski, Richard. September 2004. "Direct (pixel-based) alignment." In Image Alignment and stitching. Priliminary draft. Edition. Microsoft Research.
- Torr, P.H.S. 1998. "Model Selection for Two View Geometry: A Review." In Model Selection for Two View Geometry: A Review. Microsoft Research, USA: Microsoft Research, USA.
- Yang, Gehua, Charles V. Stewart, Michal Sofka and ChiaLing Tsai. 2006. "Automatic Robust Image Registration System: Initialization, Estimation and Decision." In Proceedings of the Forth IEEE International Conference on Computer Vision Systems (ICVS 2006).
- Zitova, B. Flusser J., 2003. "Image Registration methods: A Survey, Image and Vision Computing." 21:97
- Hartley, R. Zisserman, A. 2004. “Multiple View Geometry in Computer Vsion “(2nd Edition ed.). Cambridge University Press.
Paper Citation
in Harvard Style
Gheissari N., Kamali M., Mirshams P. and Sharafi Z. (2008). MODEL BASED GLOBAL IMAGE REGISTRATION . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 440-445. DOI: 10.5220/0001074804400445
in Bibtex Style
@conference{visapp08,
author={Niloofar Gheissari and Mostafa Kamali and Parisa Mirshams and Zohreh Sharafi},
title={MODEL BASED GLOBAL IMAGE REGISTRATION},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={440-445},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001074804400445},
isbn={978-989-8111-21-0},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - MODEL BASED GLOBAL IMAGE REGISTRATION
SN - 978-989-8111-21-0
AU - Gheissari N.
AU - Kamali M.
AU - Mirshams P.
AU - Sharafi Z.
PY - 2008
SP - 440
EP - 445
DO - 10.5220/0001074804400445