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A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method

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Abstract

This paper presents panoramic unmanned aerial vehicle (UAV) image stitching techniques based on an optimal Scale Invariant Feature Transform (SIFT) method. The image stitching representation associates a transformation matrix with each input image. In this study, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between the images. An improved Geometric Algebra (GA-SIFT) algorithm is proposed to realize fast feature extraction and feature matching work for the scanned images. The proposed GA-SIFT method can locate more feature points with greater accurately than the traditional SIFT method. The adaptive threshold value method proposed solves the limitation problem of high computation load and high cost of stitching time by greater feature points extraction and stitching work. The modified random sample consensus method is proposed to estimate the image transformation parameters and to determine the solution with the best consensus for the data. The experimental results demonstrate that the proposed image stitching method greatly increases the speed of the image alignment process and produces a satisfactory image stitching result. The proposed image stitching model for aerial images has good distinctiveness and robustness, and can save considerable time for large UAV image stitching.

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References

  1. Gu X, He M, Gu X (2016) Thermal image colorization using Markov decision processes. Memet Comp. doi:10.1007/s12293-016-0193-2

  2. Chai TY, Jin YC, Sendhoff B (2013) Evolutionary complex engineering optimization: opportunities and challenges [J]. IEEE Comput Intell Mag 8(3):12–15

    Article  Google Scholar 

  3. Quaritsch M, Kruggl K, Wischounig-Strucl D, Bhattacharya S, Shah M, Rinner B (2010) Networked UAVs as aerial sensor network for disaster management applications. Electrotec Inf Technic (e&i) 127:56–63

    Google Scholar 

  4. Yahyanejad S, Wischounig-Strucl D, Quaritsch M, Rinner B (2010) Incremental mosaicking of images from autonomous, small-scale UAVs. In: Proceedings of the 7th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE Computer Society, Boston, MA, USA, pp 329–336

  5. Yahyanejad S, Misiorny J, Rinner B (2011a) Lens distortion correction for thermal cameras to improve aerial imaging with small-scale UAVs. In: Proceedings of IEEE international symposium on robotic and sensors environments (ROSE). Montreal, QC, Canada, pp 231–236

  6. Yahyanejad S, Quaritsch M, Rinner B (2011) Incremental, orthorectified and loop-independent mosaicking of aerial images taken by micro UAVs. In: Proceedings of IEEE international symposium on robotic and sensors environments (ROSE). Montreal, QC, Canada, pp 137–142

  7. Kirstein S, Denecke A, Hasler S et al (2009) A vision architecture for unconstrained and incremental learning of multiple categories. Mem Comp 1(4):291–304

    Article  Google Scholar 

  8. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Li H, Ma JJ, Gong MG (2015) Change detection in synthetic aperture radar images based on evolutionary multi-objective optimization with ensemble learning. Mem Comp 7:275–289

    Article  Google Scholar 

  11. Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features. In: European conference on computer vision, pp 404–417

  12. Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE Conf. Comput. Vision Pattern Recognit, pp 506–513

  13. Morel JM, Yu G (2009) ASIFT: a new framework for fully affine invariant image comparison. SIAM J Imaging Sci 2(2):1–31

    Article  MathSciNet  MATH  Google Scholar 

  14. Chen M, Zhenfeng Shao ZF, Li DY, Liu J (2013) Invariant matching method for different view point angle images. Appl Optics 52(1):96–104

    Article  Google Scholar 

  15. Yu Y, Huang K, Chen W, Tan T (2012) A novel algorithm for view and illumination invariant image matching. IEEE Trans Image Process 21(1):229–240

    Article  MathSciNet  Google Scholar 

  16. Jorge RR, Eduardo BC (2007) Medical image segmentation, volume representation and registration using spheres in the geometric algebra framework. Pattern Recognit 40(1):171–188

    Article  MATH  Google Scholar 

  17. Chen X (2012) An algorithm development environment for problem-solving: software review. Mem Comp 4(2):149–161

    Article  Google Scholar 

  18. Buchholz S, Sommer G (2008) On Clifford neurons and Clifford multi-layer perceptrons. Neural Netw 21(7):925–935

    Article  MATH  Google Scholar 

  19. Brackx F, Schepper N (2005) The Clifford-Fourier transform. J Fourier Anal 11(6):669–681

    Article  MathSciNet  MATH  Google Scholar 

  20. Mohanty PK, Parhi DR (2015) A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach. Mem Comput 7:255–273

    Article  Google Scholar 

  21. Schlemmer M (2004) Fourier transformation and filter design for Clifford convolution. University of Kaiserslautern

  22. Batard T, Saint-Jean C, Berthier M (2009) A metric approach to nD images edge detection with Clifford Algebras. J Math Imaging Vis 33(3):296–312

    Article  MathSciNet  Google Scholar 

  23. Liu X, Tian Z, Chai C, Fu H (2011) Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain. Egypt J Rem Sens Space Sci 14(2):63–72

    Google Scholar 

  24. Bulow T (1999) Hypercomplex Spectral Signal representation for the processing and Analysis of Images, PhD Thesis

  25. Garg B, Sharma GK (2016) A quality-aware energy-scalable Gaussian smoothing filter for image processing applications. Microprocess Microsyst 45:1–9

    Article  Google Scholar 

  26. Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596

    Article  Google Scholar 

  27. Gustavo F, CláudioRosito J (2014) Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras. Pattern Recognit Lett 39(1):11–20

    Google Scholar 

  28. Wu XQ, Hao QS, Bu W (2014) A SIFT-based contactless palm-print verification approach using iterative RANSAC and local palm-print descriptors. Orig Res Artic Pattern Recognit 47(10):3314–3326

    Article  Google Scholar 

  29. Zhou F, Cui Y, Wang Y, Liu L (2013) Accurate and robust estimation of camera parameters using RANSAC. Orig Res Artic Optics Lasers Eng 51(3):197–212

    Article  Google Scholar 

  30. Liu J, Rettmann ME, Holmes DR (2011) A piecewise patch-to-model matching method for image-guided cardiac catheter ablation. Orig Res Artic Comput Med Imaging Graph 35(4):324–332

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Acknowledgements

This research work was supported by the China Central University Foundation, Project Number: [15D110406].

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Authors and Affiliations

  1. Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Shanghai, China

    Y. H. Zhang, X. Jin & Z. J. Wang

  2. College of Information Science and Technology, DongHua University, Shanghai, 201620, China

    Y. H. Zhang, X. Jin & Z. J. Wang

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  1. Y. H. Zhang
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  2. X. Jin
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  3. Z. J. Wang
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Correspondence to Y. H. Zhang.

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Zhang, Y.H., Jin, X. & Wang, Z.J. A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method. Memetic Comp. 9, 231–244 (2017). https://doi.org/10.1007/s12293-016-0219-9

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  • DOI: https://doi.org/10.1007/s12293-016-0219-9

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