Abstract
According to recent works, introduced by Y.Meyer [1] the decomposition models based on Total Variation (TV) appear as a very good way to extract texture from image sequences. Indeed, videos show up characteristic variations along the temporal dimension which can be catched in the decomposition framework. However, there are very few works in literature which deal with spatio-temporal decompositions. Thus, we devote this paper to spatio-temporal extension of the spatial color decomposition model. We provide a relevant method to accurately catch Dynamic Textures (DT) present in videos. Moreover, we obtain the spatio-temporal regularized part (the geometrical component), and we distinctly separate the highly oscillatory variations, (the noise). Furthermore, we present some elements of comparison between several models in denoising purpose.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Meyer, Y.: Oscillating Patterns in Image Processing and Nonlinear EvolutionEquations: The fifteenth dean jacqueline B. Lewis Memorial Lectures. American Mathematical Society, Boston (2001)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal. Physica D 60, 259–269 (1992)
Aujol, J.F., Aubert, G., Blanc-Féraud, L., Chambolle, A.: Image decomposition into a bounded variation component and an oscillating component. Journal of Mathematical Imaging and Vision 22(1), 71–88 (2005)
Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition - modeling, algorithms, and parameter selection. International Journal of Computer Vision 67(1), 111–136 (2006)
Aujol, J.F., Chambolle, A.: Dual norms and image decomposition models. International Journal of Computer Vision 63(1), 85–104 (2005)
Aujol, J.F., Kang, S.H.: Color image decomposition and restoration. J. Visual Communication and Image Representation 17(4), 916–928 (2006)
Vese, L.A., Osher, S.J.: Color texture modeling and color image decomposition in a variational-PDE approach. In: SYNASC, pp. 103–110. IEEE Computer Society, Los Alamitos (2006)
Gilles, J.: Noisy image decomposition: A new structure, texture and noise model based on local adaptivity. J. Math. Imaging Vis. 28(3), 285–295 (2007)
Bresson, X., Chan, T.: Fast minimization of the vectorial total variation norm and applications to color image processing. In: SIAM Journal on Imaging Sciences, SIIMS (submitted 2007)
Duval, V., Aujol, J.F., Vese, L.: A projected gradient algorithm for color image decomposition. Technical report, CMLA Preprint 2008-21 (2008)
Aubert, G., El-Hamidi, A., Ghannam, C., Ménard, M.: On a class of ill-posed minimization problems in image processing. Journal of Mathematical Analysis and Applications 352(1), 380–399 (2009); Degenerate and Singular PDEs and Phenomena in Analysis and Mathematical Physics
Dedeoglu, Y., Toreyin, B.U., Gudukbay, U., Cetin, A.E.: Real-time fire and flame detection in video. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. II, pp. 669–673 (2005)
Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: 4th International Conference on Computer Recognition Systems (CORES 2005), Advances in Soft Computing, Poland, pp. 17–26. Springer, Heidelberg (2005)
Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 223–230. Springer, Heidelberg (2005)
Dubois, S., Lugiez, M., Péteri, R., Ménard, M.: Adding a noise component to a color decomposition model for improving color texture extraction. In: 4th European Conference on Colour in Graphics, Imaging, and Vision. Espagne, Barcelona (2008)
Weickert, J., Steidl, G., Mrázek, P., Welk, M., Brox, T.: Diffusion filters and wavelets: What can they learn from each other? In: Paragios, N., Chen, Y., Faugeras, O. (eds.) Handbook of Mathematical Models in Computer Vision, pp. 3–16. Springer, Heidelberg (2006)
Chambolle, A.: An algorithm for total variation minimization and its applications. JMIV 20, 89–97 (2004)
Péteri, R., Huiskes, M., Fazekas, S.: Dyntex: A comprehensive database of dynamic textures (2008), http://www.cwi.nl/projects/dyntex/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lugiez, M., Ménard, M., El-Hamidi, A. (2009). Dynamic Texture Extraction and Video Denoising. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-04697-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04696-4
Online ISBN: 978-3-642-04697-1
eBook Packages: Computer ScienceComputer Science (R0)