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Robust aerial image mosaicing algorithm based on fuzzy outliers rejection

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Abstract

The use of unmanned aerial vehicles (UAVs) imagery for acquiring data is constantly evolving, due to the ease of use and low data acquisition costs. All of these made UAVs very popular with end customers and data acquisition companies. For most cases, the acquired UAV images need further more processing before being analyzed, and that because of changing of illumination condition in aerial environment and fog generated because of UAV flying speed. Image mosaicing is a practical solution for these problems; in which overlapped views of the same scene are combined to form a large image with high quality. The common problem associated with image mosaicing algorithms is the false associations (outliers) produced when defining the overlapping region between every two successive views. This article presents an image mosaicing algorithm based on efficient fuzzy technique for outliers rejection. Our proposed technique is based on using RANdom SAmpling Consensus (RANSAC) and bidirectional approaches with a fuzzy inference system in order to separate between inliers and outliers. The experimental results prove that the proposed method has good performance for aerial images and gives better results when compared with other techniques. Thus our approach is insensitive to the ordering, orientation, scale and illumination of the images.

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References

  • AerialRobotics Dataset. ftp://www.aerialrobotics.eu/. Accessed 30 Mar 2014

  • Ait-Aoudia S, Mahiou R, Djebli H, Guerrout E (2012) Satellite and aerial image mosaicing—a comparative insight. In: 16th International. Conference on Information Visualisation, Montpellier, France

  • Beckouche S, Leprince S, Sabater N, Ayoub F (2011) Robust outliers detection in image point matching. In: Computer Vision Workshops (ICCV Workshops), IEEE International Conference on IEEE, pp 180–187

  • 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

    Google Scholar 

  • Bu S, Zhao Y, Wan G, Liu Z (2016) Map2DFusion: real-time incremental UAV image mosaicing based on monocular slam. In: Intelligent Robots and Systems (IROS’16), IEEE/RSJ International Conference on IEEE, pp 4564–4571

  • Burghardt T, Damen D, Mayol-Cuevas W, Mirmehdi M (2015) Correspondence, matching and recognition. Int J Comput Vis 113(3):161–162

    Article  Google Scholar 

  • Capel D, Zisserman A (1998) Automated Mosaicing with Super-resolution Zoom. In: Robotics Research Group, Department of Engineering Science. University of Oxford Oxford OX1 3PJ, UK

  • Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  • Goshtasby AA (2005) 2-D and 3-D image registration for medical, remote sensing, and industrial applications. Wiley, Hoboken (Published simultaneously in Canada)

    Google Scholar 

  • Hafiane A, Seetharaman G, Zavidovique B (2007) Median binary pattern for textures classification. In: Proceedings of the 4th International Conference (ICIAR). Vol 4633 of Lecture Notes in Computer Science, pp 387–398, Montreal, Canada

  • Hassaballah M, Abdelmgeid AA, Hammam A (2016) Image features detection, description and matching. Springer International Publishing, Switzerland

    Book  Google Scholar 

  • Iftikhar H, Khurshid K (2011) Fusion of gabor filter and morphological operators for the detection of settlement zones in google earth satellite images. In: IEEE international conference on signal and image processing applications (ICSIPA), IEEE

  • Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved LBP under Bayesian framework. In: Proceedings of the 3rd International Conference on Image and Graphics, (ICIG), pp 306–309, Hong Kong, China

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

    Google Scholar 

  • Kim J, Kim T, Shin D, Kim SH (2016) Robust mosaicking of Uav images with narrow overlaps. Int Arch Photogram Remote Sens Spatial Inf Sci 41:879

    Article  Google Scholar 

  • Laroze M, Courtrai L, Lefèvre S (2016) Human detection from aerial imagery for automatic counting of shellfish gatherers. In: VISIGRAPP (4: VISAPP) (pp 664–671)

  • Li M, Li D, Dengke F (2012) A Study on automatic UAV image mosaic method for paroxysmal disaster. Int Arch Photogram Remote Sens Spatial Inf Sci 2012:123–128

    Article  Google Scholar 

  • Li X, Hui N, Shen H et al (2015) A robust mosaicking procedure for high spatial resolution remote sensing images. ISPRS J Photogram Remote Sens 109:108–125

    Article  Google Scholar 

  • Lin WY, Cheng MM, Lu J, Yang H, Do MN, Torr P (2014) Bilateral functions for global motion modeling. In: Proc. of European Conf. on Computer Vision, pp 341–356

  • Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  • McCormack ED, Trepanier T (2008) The use of small unmanned aircraft by the Washington State Department of Transportation. (No. WA-RD 703.1). Washington State Department of Transportation

  • Nagaraja S, Prabhakar CJ, Praveen Kumar PU (2014) Parallax effect free mosaicing of underwater video sequence based o texture features. Signal Image Process Int J (SIPIJ) 5:5

    Google Scholar 

  • Nemra A (2010) Robust Airborne 3D visual simultaneous localisation and mapping. PHD Thesis. Cranfield University

  • Ning S, Zhenhai J, Cairong Z (2007) Gender classification based on local binary pattern. J Huazhong Univ Sci Technol 35:177–181 (Chinese)

    Google Scholar 

  • Paalanen P, Kämäräinen J-K, Kälviäinen H (2009) Image based quantitative mosaic evaluation with artificial video. In: 16th Scandinavian Conference, SCIA, Oslo, Norway, pp 470–479

  • Rabin J, Delon J, Gousseau Y, Moisan L (2010) Mac-ransac:a robust algorithm for the recognition of multiple objects. In: 3D’PVT

  • Reeff M, Gerhard F, Cattin PC, Székely G (2006) Mosaicing of endoscopic placenta images. GI Jahrestagung 1:467–474

    Google Scholar 

  • Saravanan C (2010) Color image to grayscale image conversion. In: Second international conference on computer engineering and applications, vol 2, IEEE

  • Sun X et al (2016) Salient Region Detection Based on SLIC and Graph-based Segmentation. In: 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016). Atlantis Press

  • Zhengyou Z, Rachid D, Faugeras O, Quang-Tuan, Luong (1995) A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial intelligence 78(1–2):87–119

    Article  Google Scholar 

  • Zhou Z (2014) FPGA implementation of computer vision algorithm. A thesis submitted in partial satisfaction of the requirement for the degree of Master of Science in Electrical Engineering. University of California (Riverside)

  • Zhou X et al (2006) Real-time joint landmark recognition and classifier generation by an evolving fuzzy system. In: IEEE international conference on fuzzy systems, pp 1205–1212

  • Zhou X et al (2007) Autonomous visual self-localization in completely unknown environment using evolving fuzzy rule-based classifier. In: IEEE symposium on computational intelligence in security and defense applications

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Authors and Affiliations

  1. Université de Sciences et Technologie de Houari Boumedian USTHB, Alger, Algeria

    Abdelhai Lati & Noura Achour

  2. Laboratoire de Robotique, Parallélisme et Systèmes Embarqués LRPSE, BP 32, El Alia, Bab Ezzouar, Alger, 16111, Algeria

    Abdelhai Lati & Noura Achour

  3. Centre du Développement des Technologies Avancées CDTA, Cité 20 août 1956, Baba Hassen, Alger, 16303, Algeria

    Mahmoud Belhocine

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  1. Abdelhai Lati
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  2. Mahmoud Belhocine
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  3. Noura Achour
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Correspondence to Abdelhai Lati.

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Lati, A., Belhocine, M. & Achour, N. Robust aerial image mosaicing algorithm based on fuzzy outliers rejection. Evolving Systems 11, 717–729 (2020). https://doi.org/10.1007/s12530-019-09279-4

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