International Journal of CAD/CAM

Society for Computational Design and Engineering (한국CDE학회)
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Feature Based Multi-Resolution Registration of Blurred Images for Image Mosaic

  • Fang, Xianyong (Key Lab. of Intelligent Computing and Signal Processing of MOE, Anhui University) ;
  • Luo, Bin (Key Lab. of Intelligent Computing and Signal Processing of MOE, Anhui University) ;
  • He, Biao (Key Lab. of Intelligent Computing and Signal Processing of MOE, Anhui University) ;
  • Wu, Hao (Key Lab. of Intelligent Computing and Signal Processing of MOE, Anhui University)
  • Published : 2010.04.01
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Abstract

Existing methods for the registration of blurred images are efficient for the artificially blurred images or a planar registration, but not suitable for the naturally blurred images existing in the real image mosaic process. In this paper, we attempt to resolve this problem and propose a method for a distortion-free stitching of naturally blurred images for image mosaic. It adopts a multi-resolution and robust feature based inter-layer mosaic together. In each layer, Harris corner detector is chosen to effectively detect features and RANSAC is used to find reliable matches for further calibration as well as an initial homography as the initial motion of next layer. Simplex and subspace trust region methods are used consequently to estimate the stable focal length and rotation matrix through the transformation property of feature matches. In order to stitch multiple images together, an iterative registration strategy is also adopted to estimate the focal length of each image. Experimental results demonstrate the performance of the proposed method.

Keywords

References

  1. Boutellier, J., and Silvn, O. (2006) Panoramas from partially blurred video. In Proceedings of International Workshop on Intelligent Computing in Pattern Analysis/ Synthesis (IWICPAS 2006), vol. 4153, pp. 300-307.
  2. Brown, L. G. (1992) A survey of image registration techniques. ACM Computing Surveys 24, 4, 325-376. https://doi.org/10.1145/146370.146374
  3. Brown, M., and Lowe, D. G. (2007) Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74, 1, 59-73. https://doi.org/10.1007/s11263-006-0002-3
  4. Coleman, T. F., and Li, Y. (1996) An interior trust region approach for nonlinear minimization subject to bounds. SIAM Journal on Optimization 6, 418-445. https://doi.org/10.1137/0806023
  5. Fang, X., Pan, Z., He, G., and Li, L. (2006) A new method of feature based image mosaic. International Journal of Image and Graphics 6, 3, 497-510. https://doi.org/10.1142/S0219467806002380
  6. Fang, X., Pan, Z., Luo, B., Wu, F., and Guo, S. (2008) Robust image mosaic with ransac and bundle adjustment. Journal of Computational Information Systems 4, 4, 1613-1619.
  7. Fischler, M. A., and Bolles, R. C. (1987) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Readings in computer vision: issues, problems, principles, and paradigms, 726–740, Morgan Kaufmann Publishers Inc.
  8. Flusser, J., Boldys, J., and Zitov, B. (2003) Moment forms invariant to rotation and blur in arbitrary number of dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 2, 234-246. https://doi.org/10.1109/TPAMI.2003.1177154
  9. Harris, C., and Stephens, M. (1988) A combined corner and edge detection. In Proceedings of The Fourth Alvey Vision Conference, pp. 147-151.
  10. Kautsky, J., 'a, B. Z., Flusser, J., and Peters, G. (1998) Feature point detection in blurred images. In International conference of Image and Vision Computing (IVCNZ'98), pp. 103-108.
  11. Kovesi, P. D. MATLAB and Octave functions for computer vision and image processing. School of Computer Science & Software Engineering, The University of Western Australia. Available from: .
  12. Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E. (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J. on Optimization 9, 1, 112-147. https://doi.org/10.1137/S1052623496303470
  13. Levin, A., Zomet, A., Peleg, S., and Weiss, Y. (2004) Seamless image stitching in the gradient domain. In 8th European Conference on Computer Vision (ECCV 2004), T. Pajdla and J. Matas, Eds., vol. 3024 of Lecture Notes in Computer Science, Springer, pp. 377-389.
  14. Ojansivu, V., and Heikkil¨a, J. (2007) Blur invariant registration of rotated, scaled and shifted images. In The 2007 European Signal Processing Conference, pp. 1755-1759.
  15. Rav-Acha, A., Engel, G., and Peleg, S. (2008) Minimal aspect distortion (mad) mosaicing of long scenes. International Journal of Computer Vision 78, 2-3, 187-206. https://doi.org/10.1007/s11263-007-0101-9
  16. Smoky City Design, L. The panorama factory v5.2. http:// www.panoramafactory.com.
  17. Sroubek, F., and Flusser, J. (2004) Registration and fusion of blurred images. Lecture Notes in Computer Science 2004, 3211, 122-129.
  18. Szeliski, R. (2006) Image alignment and stitching: a tutorial. Foundations and Trends in Computer Graphics and Computer Vision 2, 1, 1-104. https://doi.org/10.1561/0600000009
  19. Vandewalle, P., S"usstrunk, S., and Vetterli, M. (2006) A frequency domain approach to registration of aliased images with application to superresolution. EURASIP Journal on Applied Signal Processing 2006, 1, 233-233.
  20. Wang, C., and Mahadevan, S. (2008) Manifold alignment using procrustes analysis. In ICML '08: Proceedings of the 25th international conference on Machine learning, ACM, pp. 1120-1127.
  21. Yuan, L., Sun, J., Quan, L., and Shum, H.-Y. (2007) Blurred/non-blurred image alignment using sparseness prior. In IEEE 11th International Conference on Computer Vision (ICCV 2007), IEEE, pp. 1-8.
  22. Zhou, L. (2004) Image matching using resolution pyramids with geometric constraints. Patent number US 6785427.