Total views : 237

Optimisation of Image Fusion using Feature Matching Based on SIFT and RANSAC

Affiliations

  • School of Computer Science Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India

Abstract


Background/Objectives: Image fusion is the technique which merges the input images to obtain the focused single image. A new method is proposed in this paper to optimise image fusion using feature matching based Scale Invariant Feature Transform (SIFT) and Random Sample Consensus (RANSAC). Methods/Analysis: In our proposed method, two input images are fused using Stationary Wavelet Transform to get a single focused image. Then, feature matching technique called SIFT is applied to match the corresponding features between two images. Further, RANSAC is applied to further optimise the result of SIFT and get a final fused image. Findings: Quantitative and visual results show that a highly focused and better fused image is obtained after feature matching with SIFT and further refinement with RANSAC. The proposed method is robust and independent of scale, light intensity, orientation of camera etc. Applications: The methodology for image fusion may be applied to stereo-images. Feature matching based on SIFT and RANSAC may be used to reconstruct a 3D view from stereo-images.

Keywords

Image Fusion, RANSAC, SIFT, Stationary Wavelet Transform.

Full Text:

 |  (PDF views: 235)

References


  • Ghassemian H. A review of remote sensing image fusion methods. Information Fusion. 2016; 32: 75–89.
  • James A, Dasarathy B. Medical image fusion: A survey of the state of the art. Information Fusion.2014; 19: 4–19.
  • Selvarani P, Vaithyanat V. Coarse to Fine Level Set Segmentation of SAR Imagery Based on The Brovey Transform Fusion of Optical Imagery. Research Journal of Applied Sciences. 2012; 7(7): 334–9.
  • Wan T, Zhu C,Qin Z. Multifocus image fusion based on robust principal component analysis. Pattern Recognition Letters. 2013; 34(9): 1001–8.
  • Myungjin Choi. A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Transactions on Geoscience Remote Sensing. 2006; 44(6):1672–82.
  • Toet A. Image fusion by a ratio of low-pass pyramid. Pattern Recognition Letters. 1989; 9(4); 245–53.
  • Burt P, Adelson E. The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications. 1983; 31(4):532–40.
  • Vijayarajan R, Muttan S. Discrete wavelet transform based principal component averaging fusion for medical images. AEU - International Journal of Electronics and Communications. 2015; 69(6): 896–902.
  • Liu Y, Yu F. An automatic image fusion algorithm for unregistered multiply multi-focus images. Optics Communications. 2014; 341:101–13.
  • Singh G, Singh G, Aujla GS. MHWT-A Modified Haar Wavelet Transformation for Image fusion. International Journal of Computer Applications. 2013; 79(1): 26–31.
  • Clonda D, Lina J, Goulard B. Complex Daubechies wavelets: properties and statistical image modelling. Signal Processing. 2004; 84(1): 1–23.
  • Zitova B, Flusser J. Image registration methods: A survey. Image and Vision Computing. 2003; 21(11): 977–1000.
  • Huang W, Jing Z. Evaluation of focus measures in multifocus image fusion. Pattern Recognition Letters. 2007; 28(4): 493–500.
  • Lowe D. Distictive image features from Scale-Invariant keypoints. International Journal of Computer Vision. 2004; 60(2): 91–110.
  • Ce Liu, Yuen J, Torralba A. SIFT Floe: Dense Correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011; 33(5):978–94.
  • Fischler M, Bolles R. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM. 1981; 24(6):381–95.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.