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Algorithm based on morphological component analysis and scale-invariant feature transform for image registration

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Abstract

In this paper, we proposed a registration method by combining the morphological component analysis (MCA) and scale-invariant feature transform (SIFT) algorithm. This method uses the perception dictionaries, and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance, we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.

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References

  1. PENG X, XU J, ZHOU Y, et al. Highly parallel linebased image coding for many cores [J]. IEEE Transactions on Image Processing, 2012, 21(1): 196–206.

    Article  MathSciNet  Google Scholar 

  2. YANG J, YU K, GONG Y, et al. Linear spatial pyramid matching using sparse coding for image classification [C]// Computer Vision and Pattern Recognition, 2009. Miami, Florida: IEEE, 2009: 1794–1801.

    Chapter  Google Scholar 

  3. PAL S, MITRA M. Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method [J]. Measurement, 2010, 43(2): 255–261.

    Article  Google Scholar 

  4. MISRA I, MOORTHI S M, DHAR D, et al. An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model [C]// 2012 1st International Conference on Recent Advances in Information Technology (RAIT). India: IEEE, 2012: 68–73.

    Chapter  Google Scholar 

  5. KITCHEN L, ROSENFELD A. Gray-level corner detection [R]. Maryland: Maryland Univ College Park Computer Vision Lab, 1980.

    Google Scholar 

  6. DRESCHLER L, NAGEL H H. Volumetric model and 3D trajectory of a moving car derived from monocular TV-frame sequences of a street scene [J]. Computer Graphics and Image Processing, 1982, 20(3): 199–228.

    Article  Google Scholar 

  7. LOWE D G. Distinctive image features from scaleinvariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91–110.

    Article  Google Scholar 

  8. ANDREU F, CASELLES V, DIAZ J I, et al. Some qualitative properties for the total variation flow [J]. Journal of Functional Analysis, 2002, 188(2): 516–547.

    Article  MathSciNet  MATH  Google Scholar 

  9. VESE L A, OSHER S J. Modeling textures with total variation minimization and oscillating patterns in image processing [J]. Journal of Scientific Computing, 2003, 19(1/2/3): 553–572.

    Article  MathSciNet  MATH  Google Scholar 

  10. STARCK J L, ELAD M, DONOHO D. Redundant multiscale transforms and their application for morphological component separation [J]. Advances in Imaging and Electron Physics, 2004, 132: 287–348.

    Article  Google Scholar 

  11. STARCK J L, DONOHO D L, CANDÈS E J. Astronomical image representation by the curvelet transform [J]. Astronomy & Astrophysics, 2003, 398(2): 785–800.

    Article  Google Scholar 

  12. BRUCE A G, SARDY S, TSENG P. Block coordinate relaxation methods for nonparamatric signal denoising [C]// Aerospace/Defense Sensing and Controls. USA: International Society for Optics and Photonics, 1998: 75–86.

    Google Scholar 

  13. CANDES E, DEMANET L, DONOHO D, et al. Fast discrete curvelet transforms [J]. Multiscale Modeling & Simulation, 2006, 5(3): 861–899.

    Article  MathSciNet  MATH  Google Scholar 

  14. DAUGMAN J G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters [J]. Journal of the Optical Society of America A: Optics Image Science and Vision, 1985, 2(7): 1160–1169.

    Article  Google Scholar 

  15. MIKOLAJCZYK K, SCHMID C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615–1630.

    Article  Google Scholar 

  16. SARDY S, TSENG P, BRUCE A. Robust wavelet denoising [J]. IEEE Transactions on Signal Processing, 2001, 49(6): 1146–1152.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Ludong University, Yantai, 264025, Shandong, China

    Gang Wang  (王 刚), Jingna Li  (李京娜), Qingtang Su  (苏庆堂), Xiaofeng Zhang  (张小峰), Gaohuan Lü  (吕高焕) & Honggang Wang  (王洪刚)

Authors
  1. Gang Wang  (王 刚)
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  2. Jingna Li  (李京娜)
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  3. Qingtang Su  (苏庆堂)
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  4. Xiaofeng Zhang  (张小峰)
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  5. Gaohuan Lü  (吕高焕)
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  6. Honggang Wang  (王洪刚)
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Corresponding author

Correspondence to Gang Wang  (王 刚).

Additional information

Foundation item: the National Science Foundation of China (No. 61471185), the Natural Science Foundation of Shandong Province (No. ZR2016FM21), Shandong Province Science and Technology Plan Project (No. 2015GSF116001) and Yantai City Key Research and Development Plan Project (Nos. 2014ZH157 and 2016ZH057)

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Wang, G., Li, J., Su, Q. et al. Algorithm based on morphological component analysis and scale-invariant feature transform for image registration. J. Shanghai Jiaotong Univ. (Sci.) 22, 99–106 (2017). https://doi.org/10.1007/s12204-017-1807-7

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  • DOI: https://doi.org/10.1007/s12204-017-1807-7

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