Advertisement

Visual Texture pp 255-276 | Cite as

Applications

  • Michal Haindl
  • Jiří Filip
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Advanced visual textures have a huge number of practical applications in numerous areas of applied visual information. Recent progress in computing technology, together with the newly emerging measuring devices and advances in mathematical modeling techniques, allow us to develop such sophisticated visual applications for the first time.

Keywords

Cultural Heritage Augmented Reality Gaussian Mixture Model Local Binary Pattern Visual Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Andrey, P., Tarroux, P.: Unsupervised segmentation of Markov random field modeled textured images using selectionist relaxation. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 252–262 (1998) CrossRefGoogle Scholar
  2. 2.
    Anonymous: Codex Gigas. Benedictine monastery of Podlažice in Bohemia (early 13th century). http://www.kb.se/codex-gigas/eng/
  3. 3.
    Ashikhmin, M.: Synthesizing natural textures. In: ACM Symposium on Interactive 3D Graphics, pp. 217–226 (2001) Google Scholar
  4. 4.
    Bar-Joseph, Z., El-Yaniv, R., Lischinski, D., Werman, M.: Texture mixing and texture movie synthesis using statistical learning. IEEE Trans. Vis. Comput. Graph. 7(2), 120–135 (2001) CrossRefGoogle Scholar
  5. 5.
    Brooks, S., Dodgson, N.A.: Self-similarity based texture editing. ACM Trans. Graph. 21(3), 653–656 (2002) CrossRefGoogle Scholar
  6. 6.
    Brooks, S., Dodgson, N.A.: Integrating procedural textures with replicated image editing. In: Spencer, S.N. (ed.) Proceedings of the 3rd International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia 2005, Dunedin, New Zealand, November 29–December 2, 2005, pp. 277–280. ACM, New York (2005). Google Scholar
  7. 7.
    Brooks, S., Cardle, M., Dodgson, N.A.: Enhanced texture editing using self similarity. In: VVG, pp. 231–238 (2003) Google Scholar
  8. 8.
    Grim, J., Somol, P., Haindl, M., Daneš, J.: Computer-aided evaluation of screening mammograms based on local texture models. IEEE Trans. Image Process. 18(4), 765–773 (2009). http://doi.ieeecomputersociety.org/10.1109/TIP.2008.2011168 MathSciNetCrossRefGoogle Scholar
  9. 9.
    Haindl, M.: Texture segmentation using recursive Markov random field parameter estimation. In: Bjarne, K.E., Peter, J. (eds.) Proceedings of the 11th Scandinavian Conference on Image Analysis, pp. 771–776. Pattern Recognition Society of Denmark, Lyngby, Denmark (1999). http://citeseer.ist.psu.edu/305262.html; http://www.ee.surrey.ac.uk/Research/VSSP/3DVision/virtuous/Publications/Haindl-SCIA99.ps.gz Google Scholar
  10. 10.
    Haindl, M., Filip, J.: Fast restoration of colour movie scratches. In: Kasturi, R., Laurendeau, D., Suen, C. (eds.) Proceedings of the 16th International Conference on Pattern Recognition, pp. 269–272. IEEE Comput. Soc., Los Alamitos (2002). http://dx.doi.org/10.1109/ICPR.2002.1047846 Google Scholar
  11. 11.
    Haindl, M., Hatka, M.: Near-regular texture synthesis. In: Jiang, X., Petkov, N. (eds.) Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, vol. 5702, pp. 1138–1145. Springer, Berlin (2009). http://dx.doi.org/10.1007/978-3-642-03767-2_138 CrossRefGoogle Scholar
  12. 12.
    Haindl, M., Hatka, M.: Near-regular BTF texture model. In: Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010, pp. 858–861. IEEE Computer Society CPS, Los Alamitos (2010). http://doi.ieeecomputersociety.org/10.1109/10.1109/ICPR.2010.518. Google Scholar
  13. 13.
    Haindl, M., Havlíček, V.: Texture editing using frequency swap strategy. In: Jiang, X., Petkov, N. (eds.) Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, vol. 5702, pp. 1146–1153. Springer, Berlin (2009). http://dx.doi.org/10.1007/978-3-642-03767-2_139 CrossRefGoogle Scholar
  14. 14.
    Haindl, M., Havlíček, V.: A plausible texture enlargement and editing compound Markovian model. In: Salerno, E., Cetin, A.E., Salvetti, O. (eds.) MUSCLE 2011. Lecture Notes in Computer Science, vol. 7252, pp. 138–148. Springer, Heidelberg (2012). http://dx.doi.org/10.1007/978-3-642-32436-9_12 Google Scholar
  15. 15.
    Haindl, M., Mikeš, S.: Model-based texture segmentation. Lect. Notes Comput. Sci. 3212, 306–313 (2004) CrossRefGoogle Scholar
  16. 16.
    Haindl, M., Mikeš, S.: Colour texture segmentation using modelling approach. Lect. Notes Comput. Sci. 3687, 484–491 (2005) CrossRefGoogle Scholar
  17. 17.
    Haindl, M., Mikeš, S.: Unsupervised texture segmentation using multispectral modelling approach. In: Tang, Y., Wang, S., Yeung, D., Yan, H., Lorette, G. (eds.) Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. II, pp. 203–206. IEEE Comput. Soc., Los Alamitos (2006). http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.1148 Google Scholar
  18. 18.
    Haindl, M., Mikes, S.: Unsupervised texture segmentation using multiple segmenters strategy. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. Lecture Notes in Computer Science, vol. 4472, pp. 210–219. Springer, Berlin (2007). http://dx.doi.org/10.1007/978-3-540-72523-7_22 Google Scholar
  19. 19.
    Haindl, M., Mikeš, S.: Texture segmentation benchmark. In: Lovell, B., Laurendeau, D., Duin, R. (eds.) Proceedings of the 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE Comput. Soc., Los Alamitos (2008). http://dx.doi.org/10.1109/ICPR.2008.4761118; http://doi.ieeecomputersociety.org/ CrossRefGoogle Scholar
  20. 20.
    Haindl, M., Mikeš, S.: Unsupervised mammograms segmentation. In: Lovell, B., Laurendeau, D., Duin, R. (eds.) Proceedings of the 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE Comput. Soc., Los Alamitos (2008). http://doi.ieeecomputersociety.org/10.1109/ICPR.2008.4761113 CrossRefGoogle Scholar
  21. 21.
    Haindl, M., Šimberová, S.: A scratch removal method. Kybernetika 34(4), 423–428 (1998) MATHGoogle Scholar
  22. 22.
    Haindl, M., Vácha, P.: Illumination invariant texture retrieval. In: Tang, Y., Wang, S., Yeung, D., Yan, H., Lorette, G. (eds.) Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. III, pp. 276–279. IEEE Comput. Soc., Los Alamitos (2006). http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.678 Google Scholar
  23. 23.
    Haindl, M., Žid, P.: Fast segmentation of planar surfaces in range images. In: Jain, A.K., Venkatesh, S., Lovell, B.C. (eds.) Proceedings of the 14th International Conference on Pattern Recognition, pp. 985–987. IEEE Press, Los Alamitos (1998). http://dx.doi.org/10.1109/ICPR.1998.711853 Google Scholar
  24. 24.
    Haindl, M., Žid, P.: Multimodal Range Image Segmentation (Chap.  2). I-Tech Education and Publishing, Vienna (2007) Google Scholar
  25. 25.
    Haindl, M., Grim, J., Mikes, S.: Texture defect detection. In: Computer Analysis of Images and Patterns, pp. 987–994 (2007) http://dx.doi.org/10.1007/978-3-540-74272-2_122 CrossRefGoogle Scholar
  26. 26.
    Haindl, M., Mikeš, S., Scarpa, G.: Unsupervised detection of mammogram regions of interest. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) Knowledge-Based Intelligent Information and Engineering Systems. LNAI, vol. 4694, pp. 33–40. Springer, Berlin (2007). http://dx.doi.org/10.1007/978-3-540-74829-8_5 CrossRefGoogle Scholar
  27. 27.
    Haindl, M., Žid, P., Holub, R.: Range video segmentation. In: Boashash, B., Hamila, R., Salleh, S.H.S., Bakar, S.A.R.A. (eds.) 10th International Conference on Information Sciences, Signal Processing and Their Applications, pp. 369–372. IEEE Press, Kuala Lumpur, Malaysia (2010) Google Scholar
  28. 28.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973) CrossRefGoogle Scholar
  29. 29.
    Hasegawa, T., Tsumura, N., Nakaguchi, T., Iino, K.: Photometric approach to surface reconstruction of artist paintings. J. Electron. Imaging 20, 013006 (2011) CrossRefGoogle Scholar
  30. 30.
    Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: SIGGRAPH’01, pp. 327–340. ACM, New York (2001) Google Scholar
  31. 31.
    Kautz, J., Boulos, S., Durand, F.: Interactive editing and modeling of bidirectional texture functions. In: SIGGRAPH ’07, ACM SIGGRAPH 2007 Papers, p. 53. ACM, New York (2007). http://doi.acm.org/10.1145/1275808.1276443 CrossRefGoogle Scholar
  32. 32.
    Khan, E.A., Reinhard, E., Fleming, R.W., Bülthoff, H.H.: Image-based material editing. ACM Trans. Graph. 25(3), 654–663 (2006). http://doi.acm.org/10.1145/1141911.1141937 CrossRefGoogle Scholar
  33. 33.
    Landy, M., Graham, N.: Visual perception of texture. Vis. Neurosci. 2, 1106–1118 (2004) Google Scholar
  34. 34.
    Lawrence, J., Ben-Artzi, A., DeCoro, C., Matusik, W., Pfister, H., Ramamoorthi, R., Rusinkiewicz, S.: Inverse shade trees for non-parametric material representation and editing. In: SIGGRAPH ’06, ACM SIGGRAPH 2006 Papers, pp. 735–745. ACM, New York (2006) CrossRefGoogle Scholar
  35. 35.
    Laws, K.: Rapid texture identification. In: Proc. SPIE Conf. Image Processing for Missile Guidance, pp. 376–380 (1980) CrossRefGoogle Scholar
  36. 36.
    Liang, L., Liu, C., Xu, Y.Q., Guo, B., Shum, H.Y.: Real-time texture synthesis by patch-based sampling. ACM Trans. Graph. 20(3), 127–150 (2001) CrossRefGoogle Scholar
  37. 37.
    Malzbender, T., Gelb, D., Wolters, H.: Polynomial texture maps. In: Eurographics 2001, pp. 519–528. ACM, New York (2001). Google Scholar
  38. 38.
    Manjunath, B., Chellapa, R.: Unsupervised texture segmentation using Markov random field models. IEEE Trans. Pattern Anal. Mach. Intell. 13, 478–482 (1991) CrossRefGoogle Scholar
  39. 39.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996). doi: 10.1109/34.531803 CrossRefGoogle Scholar
  40. 40.
    Mikeš, S., Haindl, M., Holub, R.: Navigation in virtual environment. In: Sablatnig, R., Hemsley, J., Krammerer, P., Zolda, E., Stockinger, J. (eds.) 2nd International Conference EVA, pp. 111–118. Austrian Comput. Soc., Vienna, Austria (2008) Google Scholar
  41. 41.
    Mikeš, S., Haindl, M., Holub, R.: National gallery in Prague. ERCIM News 86, 23–24 (2011). http://ercim-news.ercim.eu/en86/special/national-gallery-in-prague Google Scholar
  42. 42.
    Müller, G., Bendels, G.H., Klein, R.: Rapid synchronous acquisition of geometry and BTF for cultural heritage artefacts. In: The 6th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST), pp. 13–20. Eurographics Association, Geneve, Switzerland (2005) Google Scholar
  43. 43.
    Ojala, T., Pietikainen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32, 477–486 (1999) CrossRefGoogle Scholar
  44. 44.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex—new framework for empirical evaluation of texture analysis algorithms. In: 16th International Conference on Pattern Recognition, pp. 701–706 (2002) Google Scholar
  45. 45.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002) CrossRefGoogle Scholar
  46. 46.
    Panjwani, D., Healey, G.: Markov random field models for unsupervised segmentation of textured color images. IEEE Trans. Pattern Anal. Mach. Intell. 17(10), 939–954 (1995) CrossRefGoogle Scholar
  47. 47.
    Pellacini, F., Lawrence, J.: AppWand: editing measured materials using appearance-driven optimization. ACM Trans. Graph. 26(3), 54:1–54:10 (2007) CrossRefGoogle Scholar
  48. 48.
    Scarpa, G., Haindl, M., Zerubia, J.: A hierarchical texture model for unsupervised segmentation of remotely sensed images. Lect. Notes Comput. Sci. 4522, 303–312 (2007). http://dx.doi.org/10.1007/978-3-540-73040-8_31 CrossRefGoogle Scholar
  49. 49.
    Scarpa, G., Gaetano, R., Haindl, M., Zerubia, J.: Hierarchical multiple Markov chain model for unsupervised texture segmentation. IEEE Trans. Image Process. 18(8), 1830–1843 (2009). http://doi.ieeecomputersociety.org/10.1109/TIP.2009.2020534 MathSciNetCrossRefGoogle Scholar
  50. 50.
    Schwartz, C., Weinmann, M., Ruiters, R., Klein, R.: Integrated high-quality acquisition of geometry and appearance for cultural heritage. In: The 12th International Symposium on Virtual Reality, Archeology and Cultural Heritage, VAST 2011, pp. 25–32. Eurographics Association, Geneve, Switzerland (2011) Google Scholar
  51. 51.
    Schwartz, C., Weinmann, M., Ruiters, R., Zinke, A., Sarlette, R., Klein, R.: Capturing shape and reflectance of food. In: SIGGRAPH Asia 2011 Sketches, SA ’11, pp. 28:1–28:2. ACM, New York (2011) Google Scholar
  52. 52.
    Vacha, P., Haindl, M.: Image retrieval measures based on illumination invariant textural MRF features. In: CIVR ’07: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 448–454. ACM, New York (2007). http://doi.acm.org/10.1145/1282280.1282346 CrossRefGoogle Scholar
  53. 53.
    Vácha, P., Haindl, M.: Illumination invariants based on Markov random fields. In: Lovell, B., Laurendeau, D., Duin, R. (eds.) Proceedings of the 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE Comput. Soc., Los Alamitos (2008). http://doi.ieeecomputersociety.org/10.1109/ICPR.2008.4761375 CrossRefGoogle Scholar
  54. 54.
    Vácha, P., Haindl, M.: Illumination invariant and rotational insensitive textural representation. In: Bayoumi, M. (ed.) IEEE 16th Int. Conf. on Image Processing—ICIP 2009, pp. 1333–1336. IEEE Press, New York (2009). http://dx.doi.org/? CrossRefGoogle Scholar
  55. 55.
    Vácha, P., Haindl, M.: Content-based tile retrieval system. In: Hancock, E., Wilson, R., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) Structural, Syntactic, and Statistical Pattern Recognition. Lecture Notes in Computer Science, vol. 6218, pp. 434–443. Springer, Berlin/Heidelberg (2010). http://dx.doi.org/10.1007/978-3-642-14980-1_42 CrossRefGoogle Scholar
  56. 56.
    Vácha, P., Haindl, M.: Illumination invariants based on Markov random fields. In: Pattern Recognition; Recent Advances, pp. 253–272. I-Tech Education and Publishing, Zagreb, Croatia (2010). http://sciyo.com/books/show/title/pattern-recognition-recent-advances Google Scholar
  57. 57.
    Wang, X., Wang, H.: Markov random field modeled range image segmentation. In: Proceedings the Fourth International Rim Conference on Multimedia, vol. 1, pp. 86–89. IEEE Press, New York (2003) Google Scholar
  58. 58.
    Weinmann, M., Schwartz, C., Ruiters, R., Klein, R.: A multi-camera, multi-projector super-resolution framework for structured light. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), pp. 397–404. IEEE, New York (2011) CrossRefGoogle Scholar
  59. 59.
    Wiens, A.L., Ross, B.J.: Gentropy: evolving 2D textures. Comput. Graph. 26, 75–88 (2002) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Michal Haindl
    • 1
  • Jiří Filip
    • 2
  1. 1.Inst. of Information Theory & AutomationAcad. of Sciences of the Czech RepublicPragueCzech Republic
  2. 2.Inst. of Information Theory & AutomationAcad. of Sciences of the Czech RepublicPragueCzech Republic

Personalised recommendations