Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Formal Modeling of Cyber-Physical Systems: Lessons Learn from Refinement and Proof Based Methods Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Motion-Based Analysis of Dynamic Textures – A Survey

verfasst von : Ikram Bida, Saliha Aouat

Erschienen in: Advances in Computing Systems and Applications

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Textures such as grass, trees, mountains, buildings and others occupy large spaces of our visual environment. Numerous researches have been devoted to automatically analyze and characterize textures, where static textures found in single images were the first to be studied. Subsequently, this notion was extended to temporal dimension, known as dynamic texture representing variable properties in time such as flames, swaying trees, moving clouds, crowds in public places and even shadows, etc. Lately, temporal texture research is gaining a lot of attention, due to its importance as an effective component in the interpretation of video content.
This paper presents a research survey that focuses on a very captivating subject: Dynamic texture analysis, characterization and recognition. Its motivation is to give an overview of the most up-to-date analysis approaches that have been proposed to characterize then recognize temporal textures in different fields.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Virtual and Augmented Reality, from Design to Evaluation
Nächstes Kapitel -SLAM Algorithm for Autonomous Underwater Vehicle
Fußnoten
Literatur
1.
Nelson, R.C., Polana, R.: Qualitative recognition of motion using temporal texture. CVGIP: Image Underst. 56(1), 78–89 (1992)
2.
Peh, C.H., Cheong, L.F.: Exploring video content in extended spatio-temporal textures. In: In 1st European workshop on Content-Based Multimedia Indexing, Toulouse, France, pp. 147–153 (1999)
3.
Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001, vol. 2, pp. 58–63 (2001)
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)CrossRef
5.
Wang, Y., Zhu, S.C.: Modeling textured motion : particle, wave and sketch. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 213–220, October 2003
6.
Dubois, S., Peteri, R., Menard, M.: Decomposition of dynamic textures using morphological component analysis. IEEE Trans. Circuits Syst. Video Technol. 22(2), 188–201 (2012)CrossRef
7.
Chetverikov, D., Peteri, R.: A brief survey of dynamic texture description and recognition. In: Proceedings of International Conference on Computer Recognition Systems, pp. 17–26. Springer, Heidelberg (2005)
8.
Rahman, A., Murshed, M.: Temporal Texture Characterization: A Review, pp. 291–316. Springer, Heidelberg (2008)
9.
Mocofan, M., Vasiu, R.: Dynamic textures indexing using the co-occurrence matrix features. In: Proceedings of 2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 327–330, May 2012
10.
Dubois, S., Peteri, R., Ménard, M.: A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition, pp. 314–321. Springer, Heidelberg (2009)
11.
Smith, J.R., Lin, C.Y., Naphade, M.: Video texture indexing using spatio-temporal wavelets. In: Proceedings of International Conference on Image Processing, vol. 2, pp. 437–440 (2002)
12.
Bouthemy, P., Fablet, R.: Motion characterization from temporal co-occurrences of local motion-based measures for video indexing. In: Proceedings of Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), vol. 1, pp. 905–908, August 1998
13.
Phillips, W., Shah, M., Lobo, N.D.V.: Flame recognition in video. In: Proceedings of Fifth IEEE Workshop on Applications of Computer Vision, pp. 224–229 (2000)
14.
Günay, O.: Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection. Ph.D. thesis, BIlkent university (2009)
15.
Ma, Y., Cisar, P.: Event detection using local binary pattern based dynamic textures. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2009 (CVPR Workshops 2009), pp. 38–44. IEEE (2009)
16.
Komulainen, J., Hadid, A., Pietikäinen, M.: Face spoofing detection using dynamic texture. In: Asian Conference on Computer Vision, pp. 146–157. Springer, Heidelberg (2012)
17.
Hsu, W.L., Chen, T.H.: People gathering recognition based on dynamic texture detection. In: 2015 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 334–339. IEEE (2015)
18.
Li, J., Chen, L., Cai, Y.: Dynamic texture segmentation using 3-D fourier transform. In: Fifth International Conference on Image and Graphics ICIG 2009, pp. 293–298. IEEE (2009)
19.
Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 909–926 (2008)CrossRef
20.
Amiaz, T., Fazekas, S., Chetverikov, D., Kiryati, N.: Detecting regions of dynamic texture. In: International Conference on Scale Space and Variational Methods in Computer Vision, pp. 848–859. Springer, Heidelberg (2007)
21.
Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)MathSciNetCrossRef
22.
Lin, L., Xu, Y., Liang, X., Lai, J.: Complex background subtraction by pursuing dynamic spatio-temporal models. IEEE Trans. Image Process. 23(7), 3191–3202 (2014)MathSciNetCrossRef
23.
Ali, I., Mille, J., Tougne, L.: Space-time spectral model for object detection in dynamic textured background. Pattern Recogn. Lett. 33(13), 1710–1716 (2012)CrossRef
24.
Chan, A.B., Mahadevan, V., Vasconcelos, N.: Generalized stauffer-grimson background subtraction for dynamic scenes. Mach. Vis. Appl. 22(5), 751–766 (2011)CrossRef
25.
Zhang, S., Yao, H., Liu, S.: Dynamic background modeling and subtraction using spatio-temporal local binary patterns. In: Proceedings of 15th IEEE International Conference on Image Processing 2008 (ICIP 2008), pp. 1556–1559. IEEE (2008)
26.
Ramesh, V., et al.: Background modeling and subtraction of dynamic scenes. In: Proceedings of Ninth IEEE International Conference on Computer Vision, pp. 1305–1312. IEEE (2003)
27.
Tesfaldet, M., Brubaker, M.A., Derpanis, K.G.: Two-stream convolutional networks for dynamic texture synthesis. arXiv preprint arXiv:​1706.​06982 (2017)
28.
Funke, C.M., Gatys, L.A., Ecker, A.S., Bethge, M.: Synthesising dynamic textures using convolutional neural networks. arXiv preprint arXiv:​1702.​07006 (2017)
29.
Zhu, Z., You, X., Yu, S., Zou, J., Zhao, H.: Dynamic texture modeling and synthesis using multi-kernel gaussian process dynamic model. Sig. Process. 124, 63–71 (2016)CrossRef
30.
Lizarraga-Morales, R.A., Guo, Y., Zhao, G., Pietikäinen, M., Sanchez-Yanez, R.E.: Local spatiotemporal features for dynamic texture synthesis. EURASIP J. Image Video Process. 2014(1), 17 (2014)CrossRef
31.
Sheikh, Y., Haering, N., Shah, M.: Shape from dynamic texture for planes. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2285–2292. IEEE (2006)
32.
Polana, R., Nelson, R.: Temporal Texture and Activity Recognition (eds.), pp. 87–124. Springer, Dordrecht (1997)
33.
Fable, R., Bouthemy, P.: Motion-Based Feature Extraction and Ascendant Hierarchical Classification for Video Indexing and Retrieval, pp. 221–229. Springer, Heidelberg (1999)
34.
Fablet, R., Bouthemy, P., Perez, P.: Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval. IEEE Trans. Image Process. 11(4), 393–407 (2002)CrossRef
35.
Fablet, R., Bouthemy, P.: Motion recognition using spatio-temporal random walks in sequence of 2D motion-related measurements. In: Proceedings of 2001 International Conference on Image Processing (Cat. No.01CH37205), vol. 3, pp. 652–655 (2001)
36.
Fablet, R., Bouthemy, P.: Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1619–1624 (2003)CrossRef
37.
Lu, Z., Xie, W., Pei, J., Huang, J.: Dynamic texture recognition by spatio-temporal multiresolution histograms. In: Application of Computer Vision, WACV/MOTIONS 2005 Volume 1. Seventh IEEE Workshops on, vol. 2, pp. 241–246, January 2005
38.
Rahman, A., Murshed, M.: Real-time temporal texture characterization using block-based motion co-occurrence statistics. In: 2004 International Conference on Image Processing ICIP 2004, vol. 3, pp. 1593–1596, October 2004
39.
Rahman, A., Murshed, M., Dooley, L.S.: Feature weighting methods for abstract features applicable to motion based video indexing. In: Proceedings of International Conference on Information Technology: Coding and Computing ITCC 2004, vol. 1, pp. 676–680, April 2004
40.
Rahman, A., Murshed, M.: A temporal texture characterization technique using block-based approximated motion measure. IEEE Trans. Circuits Syst. Video Technol. 17(10), 1370–1382 (2007)CrossRef
41.
Rahman, A., Murshed, M.: Detection of multiple dynamic textures using feature space mapping. IEEE Trans. Circuits Syst. Video Technol. 19(5), 766–771 (2009)CrossRef
42.
Fazekas, S., Chetverikov, D.: Dynamic texture recognition using optical flow features and temporal periodicity. In: 2007 International Workshop on Content-Based Multimedia Indexing, pp. 25–32, June 2007
43.
Fazekas, S., Chetverikov, D.: A non-regular optical flow for dynamic textures. In: Fazekas, A., Hajdu, A. (eds.) KÉPAF 2007 6th conference of Hungarian Association for Image Processing and Pattern Recognition. Debrecen, KÉPAF Társ, pp. 157–164 (2007)
44.
Fazekas, S., Chetverikov, D.: Analysis and performance evaluation of optical flow features for dynamic texture recognition. Sig. Process. Image Commun. 22(7), 680–691 (2007)CrossRef
45.
Andrearczyk, V., Whelan, P.F.: Dynamic texture classification using combined co-occurrence matrices of optical flow. In: Irish Machine Vision & Image Processing Conference proceedings IMVIP, vol. 2015 (2015)
46.
Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: Marques, J.S., de la Blanca, N.P., Pina, P. (eds.) Pattern Recognition and Image Analysis: Second Iberian Conference, pp. 223–230. Springer, Heidelberg (2005)
47.
Péteri, R., Chetverikov, D.: Qualitative characterization of dynamic textures for video retrieval. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R.S., Skarbek, W., Noakes, L. (eds.) Computer Vision and Graphics: International Conference, ICCVG 2004, Proceedings, Warsaw, Poland, September 2004, pp. 33–38. Springer, Dordrecht (2006)
48.
Péteri, R.: Tracking dynamic textures using a particle filter driven by intrinsic motion information. Mach. Vis. Appl. 22(5), 781–789 (2011)CrossRef
Metadaten
Titel
Motion-Based Analysis of Dynamic Textures – A Survey
verfasst von
Ikram Bida
Saliha Aouat
Copyright-Jahr
2019
Buch
Advances in Computing Systems and Applications

Print ISBN: 978-3-319-98351-6

Electronic ISBN: 978-3-319-98352-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-98352-3_20