Abstract
There are increasing demands for image interpolation in various fields such as virtual reality, computer animation, and video transmission. The critical-point filters (CPF) we have proposed previously enable completely automatic matching of two images. In our previous method, however, it has taken a long time to compute the optimal parameter values by iterative searches. This paper proposes an improved algorithm called the enhanced critical-point filters (ECPF) where the parameters are stably computed using together the inverse mapping from the destination to the source. It takes only a second to match images whose size is 64 × 64. The algorithm is also improved in its precision by directly handling color images while the previous algorithm has taken only the intensity value into account. We apply ECPF to keyframe interpolation of video sequences. We also apply ECPF to interpolating two different views of an object to generate any intermediate views. In this case, there are many constraints that can be used to determine the mapping between the images. We propose a method to use such constraints to improve the accuracy of the mappings. As the results, image-based pseudo-3D models are easily created from a set of views without any special devices.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alvarez, L., Weickert, J., and Sanchez, J. 2000. Reliable estimation of dense optical flowfields with large displacements. International Journal of Computer Vision, 39(1):41–56.
Blanz, V. and Vetter, T. 1999. A morphable model for the synthesis of 3D faces. In Proc. SIGGRAPH'99, pp. 187–194.
Buehler, C., Bosse, M., McMillan, L., Gortler, S., and Cohen, M.F. 2001. Unstructured lumigraph rendering. In Proc. SIGGRAPH 2001, pp. 425–432.
Catmull, E. and Smith, A.R. 1980. 3-D transformations of images in scanline order. In Proc. SIGGRAPH'80, pp. 279–285.
Chen, S.E. 1995. Quick Time VR—an image-based approach to virtual environment navigation. In Proc. SIGGRAPH'95, pp. 29–38.
Chen, S.E. and Williams, L. 1993. View interpolation for image synthesis. In Proc. SIGGRAPH'93, pp. 279–288.
Debrunner, C. and Ahuja, N. 1998. Segmentation and factorizationbased motion and structure estimation for long image sequences. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(2):206–211.
Fant, K.M. and Nonaliasing, O.A. 1986. Real-time spatial transform technique. IEEE Computer Graphics and Applications, 6(3):71–80.
Gao, P. and Sederberg, T.W. 1998. A work minimization approach to image morphing. The Visual Computer, 14:399–400
Gortler, S.J., Grzeszczuk, R., Szeliski, R., and Cohen, F. 1996. The lumigraph. Proc. SIGGRAPH'96, 30:43–54.
Goshtasby, A. 1986. Piecewise linear mapping functions for image registration. Pattern Recognition, 19:459–466.
Goshtasby, A. 1987. Piecewise cubic mapping functions for image registration. Pattern Recognition, 20(5):525–533.
Gupta, N.C. and Kanal, L.N. 1995. 3-D motion estimation from motion field. Artificial Intelligence, 78:45–86.
Han, M. and Kanade, T. 1999. Perspective factorization methods for Euclidean reconstruction. Technical Report CMU-RI-TR-99-22, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, August.
Harder, R.L. and Desmarais, R.N. 1972. Interpolation using surface splines. J. Aircraft, 9(2):189–191.
Horn, B.K.P. 1986. Robot Vision. The MIT Press: Cambridge London.
Levoy, M. and Hanrahan, P. 1996. Light field rendering. Proc. SIGGRAPH'96, 30:31–42.
Luong, Q.-T. and Faugeras, O. 1997. Camera calibration, scene motion and structure recovery from point correspondences and fundamental matrices. The International Journal of Computer Vision, 22(3):261–289.
Otte, M. and Nagel, H.H. 1995. Estimation of optical flow based on higher-order spationtemporal derivatives in interlaced and non-interlaced image sequences. Artificial Intelligence, 78:5–43.
Pollefeys, M., Koch, R., and Van Gool, L. 1999. Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. International Journal of Computer Vision, 32(1):7–25.
Pratt, W.K. 1991. Digital Image Processing, 2nd edition. JohnWiley & Sons Inc.: Canada.
Russ, J.C. 1995. The Image Processing Handbook, 2nd edition. CRC Press: London.
Seitz, S. and Dyer, C. 1996.Viewmorphing. In Proc. SIGGRAPH'96, pp. 21–30.
Shinagawa, Y. and Kunii, T.L. 1998. Unconstrained automatic image matching using multiresolutional critical-point filters. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(9):994–1001.
Tomasi, C. and Kanade, T. 1992. Shape and motion from image streams under orthography: A factorization method. International Journal of Computer Vision, 9(2):137–154.
Triggs, B. 1997. Autocalibration and the absolute quadric. In proc. International Conference on Computer Vision and pattern Recognition, pp. 609–614.
Wolberg, G. 1990. Digital Image Warping. IEEE Computer Society Press: Los Alamitos, CA.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Habuka, K., Shinagawa, Y. Image Interpolation Using Enhanced Multiresolution Critical-Point Filters. International Journal of Computer Vision 58, 19–35 (2004). https://doi.org/10.1023/B:VISI.0000016145.44583.5f
Issue Date:
DOI: https://doi.org/10.1023/B:VISI.0000016145.44583.5f