Abstract
When in a pipeline a robust global motion estimation is needed, RANSAC algorithm is the usual choice. Unfortunately, since RANSAC is an iterative method based on random analysis, it is not suitable for real-time processing. This paper presents an outlier removal algorithm, which reaches a robust estimation (at least equal to RANSAC) with really low power consumption and can be employed for embedded time implementation.
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Battiato, S., Farinella, G.M., Giudice, O., Puglisi, G.: Aligning Shapes for Symbol Classification and Retrieval Multimedia Tools and Applications. Springer (2015)
Bosco, A., Bruna, A., Battiato, S., Bella G., Puglisi, G.: Digital Video Stabilization through Curve Warping Techniques. IEEE Transactions on Consumer Electronics (2008)
Battiato, S., Bruna, A., Puglisi, G.: A Robust Block Based Image/Video Registration Approach for Mobile Imaging Devices. IEEE Transactions on Multimedia (2010)
Puglisi, G., Battiato, S.: A Robust Image Alignment Algorithm for Video Stabilization Purposes. IEEE Transactions on Circuits and Systems for Video Technology (2011)
Astola, J., Kuosmanen, P.: Fundamentals of Nonlinear Digital Filtering. CRC Press. Boca Raton, New York (1997)
Ling, L., Yin, R., Wang, X.: Nonlinear filters for reducing spiky noise: 2-dimensions. In: Proceedings of the IEEE International Conference on Acoustic Speech and Signal Processing (1984)
Pearson, R.: Data cleaning for dynamic modeling and control. In: European Control Conference (1999)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24 (1981)
Matas, J., Chum, O.: Randomized RANSAC with Td, d test. Image and Vision Computing (2004)
Capel, D.: An effective bail-out test for RANSAC consensus scoring. In: Proceedings of British Machine Vision Conference (2005)
Matas, J., Chum, O.: Randomized RANSAC with sequential probability ratio test. In: Proceedings of ICCV (2005)
Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)
Tordoff, B., Murray, D.W.: Guided sampling and consensus for motion estimation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 82–96. Springer, Heidelberg (2002)
Chum, O., Matas, J.: Matching with PROSAC - progressive sample consensus. In: Proceedings of CVPR (2005)
Nister, D.: Preemptive RANSAC for live structure and motion estimation. In: Proceedings of ICCV (2003)
Battiato, S., Farinella, G.M., Messina, E., Puglisi, G.: A Robust Forensic Hash Component for Image Alignment. International Conference on Image Analysis and Processing (2011)
Chen, H.H., Liang, C., Peng, Y., Chang, H.: Integration of Digital Stabilizer With Video Codec for Digital Video Cameras. IEEE Transaction Circuits System Video Technology (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (2004)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: Computing a Local Binary Descriptor Very Fast. IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Spampinato, G., Bruna, A.: A method and device for stabilizing video sequences, related video capture apparatus and computer program product. Patent US20140204227 (2014)
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Spampinato, G., Bruna, A., Farinella, G.M., Battiato, S., Puglisi, G. (2015). Fast and Low Power Consumption Outliers Removal for Motion Vector Estimation. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_7
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DOI: https://doi.org/10.1007/978-3-319-25903-1_7
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