Skip to main content
SpringerLink
Log in

Fuzzy correlation based algorithm for UAV image mosaic construction

  • 1203: Applications of Advanced Artificial Intelligence in Multimedia and Information Security
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Aerial image mosaic construction is very important for obtaining a wide field of view with high-resolution image. After testing several image mosaicing methods, such as descriptors based algorithms and correlation based algorithms, it was observed that the common problem of those algorithms is the occurrence of numerous erroneous associations. To address this, many strategies for identifying the correct matches were suggested, such as the Random Sample Consensus (RANSAC) method; which cannot always provide efficient results. Therefore, in our work; we have proposed to detect corners as robust features in each image; then we have developed a fuzzy matching algorithm which combines known correlation measures to provide a sufficient and precise set of correct matched features required for determining the parameters of the projective transformation model. We tried the suggested technique on many scenes and found that the results maps for well-known benchmarks and are adequate in terms of recall, precision and execution speed. The results of the developed algorithm show that it efficiently overcomes problem of false matches associated with most image mosaicing techniques.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. AerialRobotics Dataset (n.d.) ftp://www.aerialrobotics.eu/.accessed: 2014-03-30

  2. Ait-Aoudia, S, Mahiou, R, Djebli, H, Guerrout, EH, (2012) Satellite and aerial image mosaicing-a comparative insight. In 2012 16th international conference on information visualisation (pp. 652-657). IEEE. https://doi.org/10.1109/IV.2012.113.

  3. Arya Mary, KJ, Priya, S (2017) Panoramic image stitching based on feature extraction and correlation. In National Conference on Future Technologies in Power, Control and Communication Systems (NFTPCOS-17) .pp. 32–39. ISSN (Online): 2319–8753

  4. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image subst 113(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  5. Beckouche, S, Leprince, S, Sabater, N, Ayoub, F, (2011) Robust outliers detection in image point matching. In 2011 IEEE international conference on computer vision workshops (ICCV workshops) (pp. 180-187). IEEE. https://doi.org/10.1109/ICCVW.2011.6130241

  6. Bede B (2013) Fuzzy inference. In: mathematics of fuzzy sets and fuzzy logic. Studies in fuzziness and soft computing, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35221-8_6

    Book  Google Scholar 

  7. Bhushan R, Sunkaria RK (2014) A novel technique of image mosaicing based on discrete wavelet transform and its performance evaluation. Int J Comput Appl 98(15):1–8. https://doi.org/10.5120/17256-7602

    Article  Google Scholar 

  8. Bostanci, E, Kanwal, N, Bostanci, B, Guzel, MS, (2017) A fuzzy brute force matching method for binary image features. arXiv preprint arXiv:1704.06018

  9. Capel D, Zisserman A (1998) Automated Mosaicing with super-resolution zoom. In proceedings. 1998 IEEE computer society conference on computer vision and pattern recognition (cat. No. 98CB36231) (pp. 885-891). IEEE. https://doi.org/10.1109/CVPR.1998.698709.

  10. Chambon S, Crouzil A (2002) Évaluation et comparaison de mesures de corrélation robustes aux occultations. Rapport de recherche, Toulouse

    Google Scholar 

  11. Friendly M (2002) A brief history of the mosaic display. J Comput Graph Stat 11(1):89–107. https://doi.org/10.1198/106186002317375631

    Article  MathSciNet  Google Scholar 

  12. Garcia, L, Diaz, J, Loaiza Co, Humberto, RG, Andrés D (2020) “Thermal and visible aerial imagery”, Mendeley Data, V2, https://doi.org/10.17632/ffgxxzx298.2.

  13. Ghannam, S, Abbott, AL (2013) Cross correlation versus mutual information for image mosaicing. International Journal of advanced computer Science and applications (IJACSA), 4(11). https://doi.org/10.14569/IJACSA.2013.041113.

  14. Ghosh D, Kaabouch N (2016) A survey on image mosaicing techniques. J Vis Commun Image Represent 34:1–11. https://doi.org/10.1016/j.jvcir.2015.10.014

    Article  Google Scholar 

  15. Goshtasby, AA, (2005) 2-D and 3-D image registration: for medical, remote sensing, and industrial applications. John Wiley & Sons

  16. Harris C, Stephens M (1988) A Combined Corner and Edge Detector .In Proceeding of the Fourth Alvey Vision Conference. Pages 147–151. https://doi.org/10.5244/C.2.23.

  17. Hassaballah M, Abdelmgeid AA, Alshazly HA (2016) Image features detection, description and matching. In image feature detectors and descriptors (pp. 11-45). Springer, Cham. https://doi.org/10.1007/978-3-319-28854-3_2

    Book  Google Scholar 

  18. Huang, SM, Huang, CC, Chou, CC (2012) Image registration among UAV image sequence and Google satellite image sub quality mismatch. In 2012 12th international conference on ITS telecommunications (pp. 311-315). IEEE. https://doi.org/10.1109/ITST.2012.6425189.

  19. Ientilucci, EJ, (2003) Using the singular value decomposition. Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology

  20. Kamarainen, JK, Paalanen, P (2009) Experimental study on fast 2d homography estimation from a few point correspondences. Research report 111, Department of Information Technology, Lappeenranta University of Technology

  21. Keane JF, Carr SS (2013) A brief history of early unmanned aircraft. Johns Hopkins Apl Tech Digest 32(3):558–571

    Google Scholar 

  22. Lan, X, Guo, B, Huang, Z, Zhang, S (2020) An improved UAV aerial image mosaic algorithm based on GMS-RANSAC. In 2020 IEEE 5th international conference on signal and image processing (ICSIP) (pp. 148-152). IEEE. https://doi.org/10.1109/ICSIP49896.2020.9339283.

  23. Lati, A, Belhocine, M, Achour, N, (2018) Efficient fuzzy based image Mosaicing algorithm for overlapped aerial images. In ICINCO (2) (pp. 239-246). https://doi.org/10.5220/0006826702290236

  24. Li, M, Li, D, Fan, D (2012) A study on automatic UAV image mosaic method for paroxysmal disaster. Int Arch Photogramm Remote Sens Spat Inf Sci pp.123–128. https://doi.org/10.5194/isprsarchives-XXXIX-B6-123-2012.

  25. Liu C, Liu H, Liu Y, Li T, Wang T (2019) Normalized cross correlation image stitching algorithm based on minimum spanning tree. Optik 179:610–616. https://doi.org/10.1016/j.ijleo.2018.10.166

    Article  Google Scholar 

  26. Lotfi A-Z (2004) Fuzzy logic systems: origin, concepts, And Trends. Computer Science Division Department of EECS UC Berkeley. Hong Kong. pp. 16–18

  27. Lowe DG (2004) Distinctive image features from scale-invariant Keypoints. Int J Comput Vis 2(60):91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  28. McCormack ED. (2008) The use of small unmanned aircraft by the Washington State Department of transportation. Washington State Transportation Commission. Dept of Transportation

  29. Mtir, IH, Kaâniche, K, Vasseur, P, Chtourou, M, (2012) False Positive Outliers Rejection for Improving Image Registration Accuracy-Application to Road Traffic Aerial Sequences. In International Conference on Informatics in Control, Automation and Robotics (Vol. 2, pp. 274–279). SCITEPRESS. https://doi.org/10.5220/0004038102740279.

  30. Nagaraja S, Prabhakar CJ, Kumar PP (2014) Parallax effect free mosaicing of subwater video sequence based on texture features. Signal Image Process 5(5):13. https://doi.org/10.5121/sipij.2014.5502

    Article  Google Scholar 

  31. Nemra, A, Aouf, N, (2010) Robust airborne 3D visual simultaneous localisation and mapping. PhD, Cranfield Defense and Security, Cranfield University

  32. Nieradka, G, Butkiewicz, BS (2009) Features stereo matching based on fuzzy logic. In IFSA/EUSFLAT Conf. (pp. 1188-1193)

  33. Patidar, D, Jain, A, (2011) Automatic image mosaicing: an approach based on FFT. In international J Sci Eng Technol (Vol. 1, no. 1, pp. 1-4). Innovative research publications.. Online ISSN: 2277-1581

  34. Peng YAN, Ru AN (2009) Improved fast corner detection algorithm based on FAST [J]. Infrared Laser Eng 38(6):1104–1108. https://doi.org/10.1049/joe.2019.0400

    Article  Google Scholar 

  35. Prathap, KSV, Jilani, SAK, Reddy, PR (2017) A real-time image mosaicing using FAST detector and FREAK descriptor. In 2017 international conference on wireless communications, signal processing and networking (WiSPNET) (pp. 2413-2418). IEEE. https://doi.org/10.1109/WiSPNET.2017.8300193.

  36. Rankov, V, Locke, RJ, Edens, RJ, Barber, PR, Vojnovic, B (2005) An algorithm for image stitching and blending. In three-dimensional and multidimensional microscopy: image acquisition and processing XII (Vol. 5701, pp. 190-199). Int Soc Opt Photon. https://doi.org/10.1117/12.590536.

  37. Renuka, D (2016) Image mosaicing using phase correlation and feature based approach: a review. Int J Eng Res, 4(1). hal-01340676

  38. Rey-Otero, I, Delbracio, M, Morel, JM, (2015) Comparing feature detectors: A bias in the repeatability criteria. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 3024–3028). IEEE. https://doi.org/10.1109/ICIP.2015.7351358

  39. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In European conference on computer vision (pp. 430-443). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744023_34

    Book  Google Scholar 

  40. Rui T, Hu Y, Yang C, Wang D, Liu X (2021) Research on fast natural aerial image mosaic. Comput Electr Eng 90:107007. https://doi.org/10.1016/j.compeleceng.2021.107007

    Article  Google Scholar 

  41. Shahla Hazim AK (2013) Using Fuzzy Logic In Image Matching. J Educ Sci 26(4):126–139. https://doi.org/10.33899/edusj.2013.89980

    Article  Google Scholar 

  42. Shen H, Jin H, Cabrera ÁA, Perer A, Zhu H, Hong JI (2020) Designing alternative representations of confusion matrices to support non-expert public substanding of algorithm performance. Proceedings ACM Human-Comput Interact 4(CSCW2):1–22. https://doi.org/10.1145/3415224

    Article  Google Scholar 

  43. Shi, J, (1994) Good features to track. In 1994 proceedings of IEEE conference on computer vision and pattern recognition (pp. 593-600). IEEE. https://doi.org/10.1109/CVPR.1994.323794

  44. Szeliski R (2006) Image alignment and stitching: a tutorial. Found Trends Comput Graph 2(1):1–104. https://doi.org/10.1561/0600000009

    Article  MathSciNet  Google Scholar 

  45. Tang J, Jiang B, Luo B, Chang CC (2015) An outlier detection method for feature point matching problem. J Inf Hiding Multimed Signal Process 6(4):728–739

    Google Scholar 

  46. Tjahjadi ME, Handoko F, Sai SS (2017) Novel image mosaicking of UAV’s imagery using collinearity condition. Int J Electr Comput Eng (IJECE) 7(3):1188–1196. https://doi.org/10.11591/ijece.v7i3.pp1188-1196

    Article  Google Scholar 

  47. Tomasi, C, Kanade, T, (1991) Detection and tracking of point. Features. Technical report CMU-CS-91-132, Carnegie, Mellon University

  48. Yang, Y, Xu, Q, Gan, Z, Liu, F (2013) A research on registration and de-ghosting algorithm in image mosaic. In 2013 international conference on wireless communications and signal processing (pp. 1-5). IEEE. https://doi.org/10.1109/WCSP.2013.6677232.

  49. Zadeh, LA, (1996). Fuzzy sets. In fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi a Zadeh (pp. 394-432). https://doi.org/10.1142/9789814261302_0021.

Download references

Acknowledgements

We would like to express our very great appreciation to all Professors of UKMO universities and specially Dr. CHAA Mourad for his valuable and constructive suggestions during the planning and development of this research work.

Author information

Authors and Affiliations

  1. Université Kasdi Merbah Ouargla (UKMO), Faculty of New information and communication technologies, BP 511, 30000, Ouargla, Algerie

    Abdelhai Lati

  2. Centre du Développement des Technologies Avancées (CDTA), Cité 20 août 1956, Baba Hassen, 16303, Alger, Algerie

    Mahmoud Belhocine

  3. Université de Sciences et Technologies de Houari Boumedian (USTHB), Laboratoire de Robotique, Parallélisme et Systèmes Embarqués (LRPSE), BP, 32, El Alia, Bab Ezzouar, 16111, Alger, Algerie

    Nouara Achour

Authors
  1. Abdelhai Lati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud Belhocine
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nouara Achour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelhai Lati.

Ethics declarations

Conflict of interest

I declare no conflicts of interest and other authors have no conflicts of interest to declare. We received a research support from Department of electronics at University Kasdi Merbah Ouargla. Algeria.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lati, A., Belhocine, M. & Achour, N. Fuzzy correlation based algorithm for UAV image mosaic construction. Multimed Tools Appl 83, 3285–3311 (2024). https://doi.org/10.1007/s11042-023-14391-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14391-4

Keywords

Search

Navigation