Abstract
We propose a dynamic texture feature-based algorithm for registering two video sequences of a rigid or nonrigid scene taken from two synchronous or asynchronous cameras. We model each video sequence as the output of a linear dynamical system, and transform the task of registering frames of the two sequences to that of registering the parameters of the corresponding models. This allows us to perform registration using the more classical image-based features as opposed to space-time features, such as space-time volumes or feature trajectories. As the model parameters are not uniquely defined, we propose a generic method to resolve these ambiguities by jointly identifying the parameters from multiple video sequences. We finally test our algorithm on a wide variety of challenging video sequences and show that it matches the performance of significantly more computationally expensive existing methods.
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Szeliski, R.: Image alignment and stitching: A tutorial. Fundamental Trends in Computer Graphics and Vision 2(1), 1–104 (2006)
Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of The Fourth Alvey Vision Conference (1988)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2003)
Brown, M., Szeliski, R., Winder, S.: Multi-image matching using multi-scale oriented patches. In: CVPR, pp. 510–517 (June 2005)
Fischler, M.A., Bolles, R.C.: RANSAC random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 26, 381–395 (1981)
Caspi, Y., Irani, M.: Spatio-temporal alignment of sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1409–1424 (2002)
Caspi, Y., Simakov, D., Irani, M.: Feature-based sequence-to-sequence matching. International Journal of Computer Vision 68(1), 53–64 (2006)
Ukrainitz, Y., Irani, M.: Aligning sequences and actions by maximizing space-time correlations. In: European Conference on Computer Vision, pp. 538–550 (2006)
Ravichandran, A., Vidal, R.: Mosaicing nonrigid dynamical scenes. In: Workshop on Dynamic Vision (2007)
Doretto, G., Chiuso, A., Wu, Y., Soatto, S.: Dynamic textures. International Journal of Computer Vision 51(2), 91–109 (2003)
Overschee, P.V., Moor, B.D.: N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, Special Issue in Statistical Signal Processing and Control, 75–93 (1994)
Chan, A., Vasconcelos, N.: Probabilistic kernels for the classification of auto-regressive visual processes. In: Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 846–851 (2005)
Vidal, R., Ravichandran, A.: Optical flow estimation and segmentation of multiple moving dynamic textures. In: Conference on Computer Vision and Pattern Recognition, vol. II, pp. 516–521 (2005)
Rugh, W.J.: Linear System Theory, 2nd edn. Prentice Hall, Englewood Cliffs (1996)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, Cambridge (2000)
http://www.wisdom.weizmann.ac.il/~vision/VideoAnalysis/Demos/Seq2Seq/
http://www.wisdom.weizmann.ac.il/~vision/VideoAnalysis/Demos/Traj2Traj
http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeCorrelations.html
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Ravichandran, A., Vidal, R. (2008). Video Registration Using Dynamic Textures. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88688-4_38
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DOI: https://doi.org/10.1007/978-3-540-88688-4_38
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