Multimedia Tools and Applications

, Volume 76, Issue 6, pp 8803–8829 | Cite as

Image mosaicing using voronoi diagram

  • A. Laraqui
  • A. Baataoui
  • A. Saaidi
  • A. Jarrar
  • Med Masrar
  • K. Satori


In this article, we propose a new method of image stitching that computes, in a robust manner, the transformation model applied to creating a panorama that is close to reality. The random selection of matching points used in existing methods, using Random Sample Consensus (RANSAC) or the threshold of the execution process (iteration number) cannot generally provide sufficient precision. Our approach, in this regard, comes to solve this problem. The calculation of the transformation model is based on the VORONOI diagram that divides images into regions to be used in the matching instead of control points. In this case, the transformation estimation will be based on the regions seeds that provide the best correlation score. Among the advantages of our method is solving problems related to outliers that can, in existing methods, affect the reliability of the mosaic. The results obtained are satisfactory in terms of stability, quality, execution time and reduction of the computational complexity.


Panorama Mosaic Voronoi Sift Geometric solution Ransac Stitching 


  1. 1.
    Alahi A, Ortiz R, Vandergheynst P (2012) Freak: fast retina keypoint. In Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on, 510–517. Ieee. doi:10.1109/CVPR.2012.6247715
  2. 2.
    Allène C, Pons JP, Keriven R (2008) Seamless image-based texture atlases using multi-band blending. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, 1–4. IEEE. doi:10.1109/ICPR.2008.4761913
  3. 3.
    Azzari P, Stefano LD, Bevilacqua A (2005) An effective real-time mosaicing algorithm apt to detect motion through background subtraction using a PTZ camera. Adv Video Signal Based Surveillance. AVSS 2005. IEEE Conf, 511–516. IEEE. doi:10.1109/AVSS.2005.1577321
  4. 4.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359. doi:10.1016/j.cviu.2007.09.014 CrossRefGoogle Scholar
  5. 5.
    Bevilacqua A, Azzari P (2007) A fast and reliable image mosaicing technique with application to wide area motion detection. Image Analysis Recognition. Springer Berlin Heidelberg. 501–512. doi:10.1007/978-3-540-74260-9_45
  6. 6.
    Brandt J (2010) Transform coding for fast approximate nearest neighbor search in high dimensions. IEEE Conf. Computer Vision Pattern Recognition. [Data file]. Retrieved from Adobe System:
  7. 7.
    Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73. doi:10.1007/s11263-006-0002-3 CrossRefGoogle Scholar
  8. 8.
    Burt PJ, Adelson EH (1983) A multiresolution spline with application to image mosaics. ACM Transactions on Graphics (TOG) 2(4):217–236. doi:10.1145/245.247 CrossRefGoogle Scholar
  9. 9.
    Choi YH, Seong YK, Choi TS (2002) Image mosaicing with automatic scene segmentation for video indexing. Int Conf Consumer Electronics, 74–75. doi:10.1109/ICCE.2002.1013933
  10. 10.
    Fang X, Zhu J, Luo B (2012) Image mosaic with relaxed motion. SIViP 6(4):647–667. doi:10.1007/s11760-010-0194-4 CrossRefGoogle Scholar
  11. 11.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395. doi:10.1145/358669.358692 MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ghosh D, Kaabouch N (2016) A survey on image mosaicing techniques. J Vis Commun Image Represent 34:1–11. doi:10.1016/j.jvcir.2015.10.014 CrossRefGoogle Scholar
  13. 13.
    Hodge VJ, Austin JA (2004) Survey of outlier detection methodologies. Artif Intell Rev 22(2):85–126. doi:10.1007/s10462-004-4304-y CrossRefMATHGoogle Scholar
  14. 14.
    Hu J, Deng W, Guo J (2011) 2D projective transformation based active shape model for facial feature location. Eighth Int Conf Fuzzy Syst Knowl Discov 4:2442–2446. doi:10.1109/FSKD.2011.6019993 Google Scholar
  15. 15.
    Huang W, Han X (2013) An improved RANSAC algorithm of color image stitching. Proc Chinese Intell Automation. 21–28. doi:10.1007/978-3-642-38466-0_3
  16. 16.
    Jalink A, McAdoo J, Halama G, Liu H (1996) CCD mosaic technique for large-field digital mammography. Med Imaging, IEEE Trans 15(3):260–267. doi:10.1109/42.500135 CrossRefGoogle Scholar
  17. 17.
    Jing N, Fan Y, Lingyi S (2013) Improved method of automatic image stitching based on SURF. Future Info Commun Technol Ubiquitous HealthCare (Ubi-HealthTech), 2013 First Int Symp, 1–5. IEEE. doi:10.1109/Ubi-HealthTech.2013.6708059
  18. 18.
    Kim BS, Lee SH, Cho NI (2011) Real-time panorama canvas of natural images. Consumer Electronics, IEEE Trans 57(4):1961–1968. doi:10.1109/TCE.2011.6131177 CrossRefGoogle Scholar
  19. 19.
    Laraqui A, Saaidi A, Jarrar A, Satori K (2014) Image stitching based on the geometric solution. Info Sci Tech (CIST), Third IEEE Int Colloquium IEEE, 340–344. doi:10.1109/CIST.2014.7016643
  20. 20.
    Laraqui M, Saaidi A, Mouhib A, Abarkan M (2015) Images matching using voronoï regions propagation. 3D Res 6(3):1–16. doi:10.1007/s13319-015-0056-5 CrossRefGoogle Scholar
  21. 21.
    Lhuillier M, Quan L (2002) Quasi-dense reconstruction from image sequence. In Computer Vision—ECCV 2002, 125–139. Springer Berlin Heidelberg. doi:10.1007/3-540-47967-8_9
  22. 22.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60:91–110. doi:10.1023/B:VISI.0000029664.99615.94 CrossRefGoogle Scholar
  23. 23.
    Ma X, Liu D, Zhang J, Xin J (2015) A fast affine-invariant features for image stitching under large viewpoint changes. Neurocomputing 151:1430–1438. doi:10.1016/j.neucom.2014.10.045 CrossRefGoogle Scholar
  24. 24.
    Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In Computer Vision—ECCV 2002, 128–142. Springer Berlin Heidelberg. doi:10.1007/3-540-47969-4_9
  25. 25.
    Montijano E, Martinez S, Sagues C (2015) Distributed robust consensus using RANSAC and dynamic opinions. Control Syst Technol, IEEE Trans 23(1):150–163. doi:10.1109/TCST.2014.2317771 CrossRefGoogle Scholar
  26. 26.
    Raguram R, Frahm JM, Pollefeys M (2008) A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. vol. 5303 LNCS, no. PART 2, 500–513. doi: 10.1007/978-3-540-88688-4_37
  27. 27.
    Rosten E, Porter R, Drummond T (2010) Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119. doi:10.1109/TPAMI.2008.275 CrossRefGoogle Scholar
  28. 28.
    Saaidi A, Tairi H, Satori K (2006) Fast stereo matching using rectification and correlation techniques. ISCCSP, Second Int Symp Commun, Control Signal Proc, 1–4Google Scholar
  29. 29.
    Sali E, Wolfson H (1992) Texture classification in aerial photographs and satellite data. Int J Remote Sensing, 3395–3408. doi:10.1080/01431169208904130
  30. 30.
    Sooknanan K, Kokaram A, Corrigan D, Baugh G, Harte N, Wilson J (2012) Indexing and selection of well-lit details in underwater video mosaics using vignetting estimation. In OCEANS, 2012-Yeosu, 1–7. IEEE. doi:10.1109/OCEANS-Yeosu.2012.6263541
  31. 31.
    Szeliski R (2002) Video mosaics for virtual environments, IEEE Comput Graphics Appl, 22–30. doi:10.1109/38.486677
  32. 32.
    Trajković M, Hedley M (1998) Fast corner detection. Image Vis Comput 16(2):75–87. doi:10.1016/S0262-8856(97)00056-5 CrossRefGoogle Scholar
  33. 33.
    Wang X, Ying X, Liu Y-J, Xin S-Q, Wang W, Gu X, Mueller-Wittig W, He Y (2015) Intrinsic computation of centroidal Voronoi tessellation (CVT) on meshes. Comput Des, 51–61. doi:10.1016/j.cad.2014.08.023
  34. 34.
    Wei GQ, Qian J, Schramm HF, Novak CL (2003) Method for intensity correction in CR mosaic by combined nonlinear and linear transformations. Med Imaging 2003. Int Soc Optics Photonics. 979–985. doi:10.1117/12.480830
  35. 35.
    Yu G, Morel JM (2011) Asift: an algorithm for fully affine invariant comparison. Image Proc On Line, 2011. doi:10.5201/
  36. 36.
    Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000. doi:10.1016/S0262-8856(03)00137-9 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • A. Laraqui
    • 1
  • A. Baataoui
    • 1
  • A. Saaidi
    • 1
    • 2
  • A. Jarrar
    • 1
    • 3
  • Med Masrar
    • 1
  • K. Satori
    • 1
  1. 1.LIIAN, Computer Science Department, Faculty of Sciences Dhar-MahrazSidi Mohamed Ben Abdellah UniversityAtlas-FesMorocco
  2. 2.LSI, Department of Mathematics, Physics and Informatics, Polydisciplinary Faculty of TazaSidi Mohamed Ben Abdellah UniversityTazaMorocco
  3. 3.LSO, Mathematics Department, Faculty of Sciences Dhar-MahrazSidi Mohamed Ben Abdellah UniversityAtlas-FesMorocco

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