nips nips2005 nips2005-108 nips2005-108-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Antoni B. Chan, Nuno Vasconcelos
Abstract: A dynamic texture is a video model that treats a video as a sample from a spatio-temporal stochastic process, specifically a linear dynamical system. One problem associated with the dynamic texture is that it cannot model video where there are multiple regions of distinct motion. In this work, we introduce the layered dynamic texture model, which addresses this problem. We also introduce a variant of the model, and present the EM algorithm for learning each of the models. Finally, we demonstrate the efficacy of the proposed model for the tasks of segmentation and synthesis of video.
[1] B. K. P. Horn. Robot Vision. McGraw-Hill Book Company, New York, 1986.
[2] B. Horn and B. Schunk. Determining optical flow. Artificial Intelligence, vol. 17, 1981.
[3] B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. Proc. DARPA Image Understanding Workshop, 1981.
[4] J. Barron, D. Fleet, and S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, vol. 12, 1994.
[5] J. Wang and E. Adelson. Representing moving images with layers. IEEE Trans. on Image Processing, vol. 3, September 1994.
[6] B. Frey and N. Jojic. Estimating mixture models of images and inferring spatial transformations using the EM algorithm. In IEEE Conference on Computer Vision and Pattern Recognition, 1999.
[7] G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto. Dynamic textures. International Journal of Computer Vision, vol. 2, pp. 91-109, 2003.
[8] G. Doretto, D. Cremers, P. Favaro, and S. Soatto. Dynamic texture segmentation. In IEEE International Conference on Computer Vision, vol. 2, pp. 1236-42, 2003.
[9] P. Saisan, G. Doretto, Y. Wu, and S. Soatto. Dynamic texture recognition. In IEEE Conference on Computer Vision and Pattern Recognition, Proceedings, vol. 2, pp. 58-63, 2001.
[10] A. B. Chan and N. Vasconcelos. Probabilistic kernels for the classification of auto-regressive visual processes. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 846-51, 2005.
[11] S. Geman and D. Geman. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6(6), pp. 72141, 1984.
[12] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, vol. 39, pp. 1-38, 1977.
[13] A. B. Chan and N. Vasconcelos. The EM algorithm for layered dynamic textures. Technical Report SVCL-TR-2005-03, June 2005. http://www.svcl.ucsd.edu/.
[14] A. B. Chan and N. Vasconcelos. Mixtures of dynamic textures. In IEEE International Conference on Computer Vision, vol. 1, pp. 641-47, 2005.
[15] R. H. Shumway and D. S. Stoffer. An approach to time series smoothing and forecasting using the EM algorithm. Journal of Time Series Analysis, vol. 3(4), pp. 253-64, 1982.
[16] S. Roweis and Z. Ghahramani. A unifying review of linear Gaussian models. Neural Computation, vol. 11, pp. 305-45, 1999.
[17] http://www.wsdot.wa.gov
[18] http://www.svcl.ucsd.edu/∼abc/nips05/