Skip to main content
SpringerLink
Log in

Applications

  • Chapter
Visual Texture

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 2065 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://mosaic.utia.cas.cz.

References

  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)

    Article  Google Scholar 

  2. Anonymous: Codex Gigas. Benedictine monastery of Podlažice in Bohemia (early 13th century). http://www.kb.se/codex-gigas/eng/

  3. Ashikhmin, M.: Synthesizing natural textures. In: ACM Symposium on Interactive 3D Graphics, pp. 217–226 (2001)

    Google Scholar 

  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)

    Article  Google Scholar 

  5. Brooks, S., Dodgson, N.A.: Self-similarity based texture editing. ACM Trans. Graph. 21(3), 653–656 (2002)

    Article  Google Scholar 

  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. Brooks, S., Cardle, M., Dodgson, N.A.: Enhanced texture editing using self similarity. In: VVG, pp. 231–238 (2003)

    Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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. Haindl, M., Mikeš, S.: Model-based texture segmentation. Lect. Notes Comput. Sci. 3212, 306–313 (2004)

    Article  Google Scholar 

  16. Haindl, M., Mikeš, S.: Colour texture segmentation using modelling approach. Lect. Notes Comput. Sci. 3687, 484–491 (2005)

    Article  Google Scholar 

  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. 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. 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/

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  21. Haindl, M., Šimberová, S.: A scratch removal method. Kybernetika 34(4), 423–428 (1998)

    MATH  Google Scholar 

  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. 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. Haindl, M., Žid, P.: Multimodal Range Image Segmentation (Chap. 2). I-Tech Education and Publishing, Vienna (2007)

    Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  29. Hasegawa, T., Tsumura, N., Nakaguchi, T., Iino, K.: Photometric approach to surface reconstruction of artist paintings. J. Electron. Imaging 20, 013006 (2011)

    Article  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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

    Article  Google Scholar 

  33. Landy, M., Graham, N.: Visual perception of texture. Vis. Neurosci. 2, 1106–1118 (2004)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  35. Laws, K.: Rapid texture identification. In: Proc. SPIE Conf. Image Processing for Missile Guidance, pp. 376–380 (1980)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  37. Malzbender, T., Gelb, D., Wolters, H.: Polynomial texture maps. In: Eurographics 2001, pp. 519–528. ACM, New York (2001).

    Google Scholar 

  38. Manjunath, B., Chellapa, R.: Unsupervised texture segmentation using Markov random field models. IEEE Trans. Pattern Anal. Mach. Intell. 13, 478–482 (1991)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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. 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. Ojala, T., Pietikainen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32, 477–486 (1999)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  47. Pellacini, F., Lawrence, J.: AppWand: editing measured materials using appearance-driven optimization. ACM Trans. Graph. 26(3), 54:1–54:10 (2007)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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/?

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  59. Wiens, A.L., Ross, B.J.: Gentropy: evolving 2D textures. Comput. Graph. 26, 75–88 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inst. of Information Theory & Automation, Acad. of Sciences of the Czech Republic, Prague, Czech Republic

    Michal Haindl

  2. Inst. of Information Theory & Automation, Acad. of Sciences of the Czech Republic, Pod Vodárenskou věží 4, Prague, 182 08, Czech Republic

    Jiří Filip

Authors
  1. Michal Haindl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiří Filip
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Haindl, M., Filip, J. (2013). Applications. In: Visual Texture. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4902-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4902-6_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4901-9

  • Online ISBN: 978-1-4471-4902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation