Abstract
In exemplar based texture synthesis methods one of the major difficulties is to synthesize correctly the wide diversity of texture images. So far the proposed methods tend to have satisfying results for specific texture classes and fail for others. Statistics-based algorithms present good results when synthesizing textures that have few geometric structures and are able to preserve a complex statistical model of the sample texture. On the other hand, non-parametric patch-based methods have the ability to reproduce faithfully highly structured textures but lack a mechanism to preserve its global statistics. Furthermore, they are strongly dependent on a patch size that is decided manually. In this paper we propose a multiscale approach able to combine advantages of both strategies and avoid some of their drawbacks. The texture is modeled at each scale as a spatially variable Gaussian vector in the patch space, which allows to fix a patch size fairly independent of the texture.
Chapter PDF
Similar content being viewed by others
References
Ashikhmin, M.: Synthesizing natural textures. In: Proceedings of the 2001 Symposium on Interactive 3D graphics, pp. 217–226. ACM (2001)
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH, pp. 341–346 (2001)
Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: IEEE ICCV, pp. 1033–1038 (1999)
Aguerrebere, C., Gousseau, Y., Tartavel, G.: Exemplar-based Texture Synthesis: the Efros-Leung Algorithm. IPOL 3, 223–241 (2013). http://dx.doi.org/10.5201/ipol.2013.59
Galerne, B., Gousseau, Y., Morel, J.-M.: Random phase textures: Theory and synthesis. IEEE Transactions in Image Processing (2010)
Galerne, B., Gousseau, Y., Morel, J.-M.: Micro-Texture Synthesis by Phase Randomization. IPOL (2011). http://dx.doi.org/10.5201/ipol.2011.ggm_rpn
Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: SIGGRAPH, New York, NY, USA, pp. 229–238 (1995)
Briand, T., Vacher, J., Galerne, B., Rabin, J.: The Heeger and Bergen Pyramid Based Texture Synthesis Algorithm. IPOL 4, 276–299 (2014). http://dx.doi.org/10.5201/ipol.2014.79
Julesz, B.: Visual pattern discrimination. IEEE Trans. Inf. Theory 8(2), 84–92 (1962)
Kwatra, V., Essa, I., Bobick, A., Kwatra, N.: Texture optimization for example-based synthesis. In: ACM Transactions on Graphics (TOG), vol. 24, pp. 795–802. ACM (2005)
Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. In: ACM Transactions on Graphics (TOG), vol. 22, pp. 277–286. ACM (2003)
Liang, L., Liu, C., Xu, Y.-Q., Guo, B., Shum, H.-Y.: Real-time texture synthesis by patch-based sampling. ACM Transactions on Graphics 20(3), 127–150 (2001)
Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. IJCV 40(1), 49–70 (2000)
Raad, L., Desolneux, A., Morel, J.M.: Locally gaussian exemplar based texture synthesis. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4667–4671. IEEE, October 2014
Peyré, G.: Sparse modeling of textures. Journal of Mathematical Imaging and Vision 34(1), 17–31 (2009)
Tartavel, G., Gousseau, Y., Peyré, G.: Variational texture synthesis with sparsity and spectrum constraints. Journal of Mathematical Imaging and Vision (2014)
Wei, L.-Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: SIGGRAPH, pp. 479–488 (2000)
Wei, L.Y., Lefebvre, S., Kwatra, V., Turk, G.: State of the art in example-based texture synthesis. In: Eurographics 2009, State of the Art Report, EG-STAR, pp. 93–117. Eurographics Association (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Raad, L., Desolneux, A., Morel, JM. (2015). Multiscale Exemplar Based Texture Synthesis by Locally Gaussian Models. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)