Automatic Panoramic Image Stitching using Invariant Features
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This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.
Keywordsmulti-image matching stitching recognition
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- Agarwala, A., Dontcheva, M., Agarwala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., and Cohen, M. 2004. Interactive digital photomontage. In ACM Transactions on Graphics (SIGGRAPH'04).Google Scholar
- Bascle, B., Blake, A., and Zisserman, A. 1996. Motion deblurring and super-resolution from and image sequence. In Proceedings of the 4th European Conference on Computer Vision (ECCV96). Springer-Verlag, pp. 312–320.Google Scholar
- Beis, J. and Lowe, D. 1997. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In Proceedings of the Interational Conference on Computer Vision and Pattern Recognition (CVPR97). pp. 1000–1006.Google Scholar
- Brown, M. and Lowe, D. 2003. Recognising panoramas. In Proceedings of the 9th International Conference on Computer Vision (ICCV03). Nice, vol. 2, pp. 1218–1225.Google Scholar
- Brown, D. 1971. Close-range camera calibration. Photogrammetric Engineering. 37(8):855–866.Google Scholar
- Brown, M., Szeliski, R., and Winder, S. 2005. Multi-image matching using multi-scale oriented patches. In Proceedings of the Interational Conference on Computer Vision and Pattern Recognition (CVPR05). San Diego.Google Scholar
- Chen, S. 1995. Quick Time VR–-An image-based approach to virtual environment navigation. In SIGGRAPH'95. vol. 29, pp. 29–38.Google Scholar
- Capel, D. and Zisserman, A. 1998. Automated mosaicing with super-resolution zoom. In Proceedings of the Interational Conference on Computer Vision and Pattern Recognition (CVPR98). pp. 885–891.Google Scholar
- Davis, J. 1998. Mosaics of scenes with moving objects. In Proceedings of the Interational Conference on Computer Vision and Pattern Recognition (CVPR98). pp. 354–360.Google Scholar
- Debevec, P. and Malik, J. 1997. Recovering high dynamic range radiance maps from photographs. Computer Graphics. 31:369–378.Google Scholar
- Goldman, D.B. and Chen, J.H. 2005 Vignette and exposure calibation and compensation. In Proceedings of the 10th International Conference on Computer Vision (ICCV05). pp. I:899–906.Google Scholar
- Harris, C. 1992. Geometry from visual motion. In Blake, A. and Yuille, A., (eds.), Active Vision. MIT Press, pp. 263–284.Google Scholar
- Huber P.J. 1981. Robust Statistics. Wiley.Google Scholar
- Hartley, R. and Zisserman, A. 2004. Multiple View Geometry in Computer Vision. 2nd edn. Cambridge University Press, ISBN: 0521540518.Google Scholar
- Irani, M. and Anandan, P. 1999. About direct methods. In Triggs, B., Zisserman, A., and Szeliski, R. (eds.), Vision Algorithms: Theory and Practice. number 1883 in LNCS. Springer-Verlag, Corfu, Greece, pp. 267–277.Google Scholar
- Meehan, J. 1990. Panoramic Photography. Amphoto Books.Google Scholar
- Milgram, D. 1975. Computer methods for creating photomosaics. IEEE Transactions on Computers. C-24 (11):1113–1119.Google Scholar
- Microsoft Digital Image Pro. http://www.microsoft.com/products/imaging.
- Realviz. http://www.realviz.com.
- Seetzen, H., Heidrich, W., Stuerzlinger, W., Ward, G., Whitehead, L., Trentacoste, M., Ghosh, A., and Vorozcovs, A. 2004. High dynamic range display systems. In ACM Transactions on Graphics (SIGGRAPH'04).Google Scholar
- Szeliski, R. and Kang, S. 1995. Direct methods for visual scene reconstruction. In IEEE Workshop on Representations of Visual Scenes. Cambridge, MA, pp. 26–33.Google Scholar
- Szeliski, R. and Shum, H. 1997. Creating full view panoramic image mosaics and environment maps. Computer Graphics (SIGGRAPH'97). 31(Annual Conference Series):251–258.Google Scholar
- Shi, J. and Tomasi, C. 1994. Good features to track. In Proceedings of the Interational Conference on Computer Vision and Pattern Recognition (CVPR94). Seattle.Google Scholar
- Sivic, J. and Zisserman, A. 2003. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the 9th International Conference on Computer Vision (ICCV03).Google Scholar
- Szeliski, R. 2004. Image alignment and stitching: A tutorial. Technical Report MSR-TR-2004-92, Microsoft Research.Google Scholar
- Triggs, W., McLauchlan, P., Hartley, R., and Fitzgibbon, A. 1999. Bundle adjustment: A modern synthesis. In Vision Algorithms: Theory and Practice. number 1883 in LNCS. Springer-Verlag. Corfu, Greece, pp. 298–373.Google Scholar
- Uyttendaele, M., Eden, A., and Szeliski, R. 2001. Eliminating ghosting and exposure artifacts in image mosaics. In Proceedings of the Interational Conference on Computer Vision and Pattern Recognition (CVPR01). Kauai, Hawaii, vol. 2, pp. 509–516.Google Scholar
- Zoghlami, I., Faugeras, O., and Deriche, R. 1997. Using geometric corners to build a 2D mosaic from a set of images. In Proceedings of the International Conference on Computer Vision and Pattern Recognition, Puerto Rico. IEEE.Google Scholar