Adelson, E. H. and Bergen, J. R. 1985. Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America, A2(2):284–299. Google Scholar
Amit, Y. 1993. Anon-linear variational problem for image matching. unpublished manuscript (from Newton Institute).
Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2(3):283–310. Google Scholar
Bajcsy, R. and Broit, C. 1982. Matching of deformed images. In Sixth International Conference on Pattern Recognition (ICPRs'82), IEEE Computer Society Press: Munich, Germany, pp. 351–353. Google Scholar
Bajcsy, R. and Kovacic, S. 1989. Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing, 46(1):1–21. Google Scholar
Barnard, S. T. and Fischler, M. A. 1982. Computational stereo. Computing Surveys, 14(4):553–572. Google Scholar
Barron, J. L., Fleet, D. J., and Beauchemin, S. S. 1994. Performance of optical flow techniques. International Journal of Computer Vision, 12(1):43–77. Google Scholar
Beier, T. and Neely, S. 1992. Feature-based image metamorphosis. Computer Graphics (SIGGRAPHs'92), 26(2):35–42. Google Scholar
Bergen, J. R., Anandan, P., Hanna, K. J., and Hingorani, R. 1992. Hierarchical model-based motion estimation. In Second European Conference on Computer Vision (ECCVs'92), Santa Margherita Liguere, Springer-Verlag: Italy, pp. 237–252. Google Scholar
Beymer, D., Shashua, A., and Poggio, T. 1993. Example based image analysis and synthesis. A. I. Memo 1431, Massachusetts Institute of Technology.
Blake, A., Curwen, R., and Zisserman, A. 1993. A framework for spatio-temporal control in the tracking of visual contour. International Journal of Computer Vision, 11(2):127–145. Google Scholar
Bolles, R. C., Baker, H. H., and Marimont, D. H. 1987. Epipolar-plane image analysis: An approach to determining structure from motion. International Journal of Computer Vision, 1:7–55. Google Scholar
Brown, L. G. 1992. A survey of image registration techniques. Computing Surveys, 24(4):325–376. Google Scholar
Burr, D. J. 1981. A dynamic model for image registration. Computer Graphics and Image Processing, 15(2):102–112. Google Scholar
Burt, P. J. and Adelson, E. H. 1983. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, COM-31(4):532–540. Google Scholar
Carlbom, I., Terzopoulos, D., and Harris, K. M. 1991. Reconstructing and visualizing models of neuronal dendrites. In Scientific Visualization of Physical Phenomena, N. M. Patrikalakis (Ed.), Springer-Verlag: New York, pp. 623–638. Google Scholar
Dhond, U. R. and Aggarwal, J. K. 1989. Structure from stereo—A review. IEEE Transactions on Systems, Man, and Cybernetics, 19(6):1489–1510. Google Scholar
Dreschler, L. and Nagel, H.-H. 1982. Volumetric model and 3D trajectory of a moving car derived from monocular TV frame sequences of a stree scene. Computer Graphics and Image Processing, 20:199–228. Google Scholar
Enkelmann, W. 1988. Investigations of multigrid algorithms for estimation of optical flow fields in image sequences. Computer Vision, Graphics, and Image Processing, pp. 150–177.
Farin, G. E. 1992. Curves and Surfaces for Computer Aided Geometric Design. Academic Press: Boston, Massachusetts, 3rd edition. Google Scholar
Faugeras, O. D. 1992. What can be seen in three dimensions with an uncalibrated stereo rig? In Second European Conference on Computer Vision (ECCVs'92), Santa Margherita Liguere, Springer-Verlag: Italy, pp. 563–578. Google Scholar
Fleet, D. and Jepson, A. 1990. Computation of component image velocity from local phase information. International Journal of Computer Vision, 5:77–104. Google Scholar
Fuh, C.-S. and Maragos, P. 1991. Motion displacement estimation using an affine model for image matching. Optical Engineering, 30(7):881–887. Google Scholar
Geiger, D., Ladendorf, B., and Yuille, A. 1992. Occlusions and binocular stereo. In Second European Conference on Computer Vision (ECCVs'92), Santa Margherita Liguere, Springer-Verlag, Italy, pp. 425–433. Google Scholar
Gennert, M. A. 1988. Brightness-based stereo matching. In Second International Conference on Computer Vision (ICCVs'88), IEEE Computer Society Press: Tampa, Florida, pp. 139–143. Google Scholar
Goshtasby, A. 1986. Piecewise linear mapping functions for image registration. Pattern Recognition, 19(6):459–466. Google Scholar
Goshtasby, A. 1988. Image registration by local approximation methods. Image and Vision Computing, 6(4):255–261. Google Scholar
Hanna, K. J. 1991. Direct multi-resolution estimation of ego-motion and structure from motion. In IEEE Workshop on Visual Motion, IEEE Computer Society Press: Princeton, New Jersey, pp. 156–162. Google Scholar
Hartley, R. and Gupta, R. 1993. Computing matched-epipolar projections. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRs'93), IEEE Computer Society Press: New York, pp. 549–555. Google Scholar
Hartley, R., Gupta, R., and Chang, T. 1992. Stereo from uncalibrated cameras. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRs'92), IEEE Computer Society Press: Champaign, Illinois, pp. 761–764,. Google Scholar
Heeger, D. J. 1987. Optical flow from spatiotemporal filters. In First International Conference on Computer Vision (ICCVs'87), IEEE Computer Society Press: London, England, pp. 181–190. Google Scholar
Hildreth, E. C. 1986. Computing the velocity field along contours. In Motion: Representation and Perception, N. I. Badler and J. K. Tsotsos (Eds.), North-Holland, New York, pp. 121–127.
Horn, B. K. P. and Schunck, B. G. 1981. Determining optical flow. Artificial Intelligence, 17:185–203. Google Scholar
Horn, B. K. P. and Weldon, E. J., Jr. 1988. Direct methods for recovering motion. International Journal of Computer Vision, 2(1):51–76. Google Scholar
Kass, M., Witkin, A., and Terzopoulos, D. 1988. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321–331. Google Scholar
Koenderink, J. J. and van Doorn, A. J. 1991. Affine structure from motion. Journal of the Optical Society of America A, 8:377–385,538. Google Scholar
Le Gall, D. 1991. MPEG: A video compression standard for multimedia applications. Communications of the ACM, 34(4):44–58. Google Scholar
Lucas, B. D. 1984. Generalized Image Matching by the Method of Differences. Ph. D. Thesis, Carnegie Mellon University.
Lucas, B. D. and Kanade, T. 1981. An iterative image registration technique with an application in stereo vision. In Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), Vancouver, pp. 674–679.
Manmatha, R. and Oliensis, J. 1992. Measuring the affine transform —I: Scale and rotation. Technical Report 92-74, University of Massachussets, Amherst, Massachussets. Google Scholar
Matthies, L. H., Szeliski, R., and Kanade, T. 1989. Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3:209–236. Google Scholar
Menet, S., Saint-Marc, P., and Medioni, G. 1990. B-snakes: implementation and applications to stereo. In Image Understanding Workshop, Morgan Kaufmann Publishers: Pittsburgh, Pennsylvania, pp. 720–726. Google Scholar
Mohr, R., Veillon, L., and Quan, L. 1993. Relative 3D reconstruction using multiple uncalibrated images. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRs'93), New York, pp. 543–548.
Nagel, H.-H. 1987. On the estimation of optical flow: Relations between different approaches and some new results. Artificial Intelligence, 33:299–324. Google Scholar
Okutomi, M. and Kanade, T. 1992. A locally adaptive window for signal matching. International Journal of Computer Vision, 7(2):143–162. Google Scholar
Okutomi, M. and Kanade, T. 1993. A multiple baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4):353–363. Google Scholar
Poggio, T., Torre, V., and Koch, C. 1985. Computational vision and regularization theory. Nature, 317(6035):314–319. Google Scholar
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. 1992. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press: Cambridge, England, 2nd edition. Google Scholar
Quam, L. H. 1984. Hierarchical warp stereo. In Image Understanding Workshop, Science Applications International Corporation: New Orleans, Louisiana, pp. 149–155. Google Scholar
Rehg, J. and Witkin, A. 1991. Visual tracking with deformation models. In IEEE International Conference on Robotics and Automation, IEEE Computer Society Press: Sacramento, California, pp. 844–850. Google Scholar
Sethi, I. K. and Jain, R. 1987. Finding trajectories of feature points in a monocular image sequence. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9(1):56–73. Google Scholar
Shi, J. and Tomasi, C. 1994. Good features to track. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRs'94), IEEE Computer Society: Seattle, Washington, pp. 593–600. Google Scholar
Simoncelli, E. P., Adelson, E. H., and Heeger, D. J. 1991. Probability distributions of optic flow. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRs'91), IEEE Computer Society Press: Maui, Hawaii, pp. 310–315. Google Scholar
Singh, A. 1990. An estimation-theoretic framework for image-flow computation. In Third International Conference on Computer Vision (ICCVs'90), IEEE Computer Society Press: Osaka, Japan, pp. 168–177. Google Scholar
Szeliski, R. 1989. Bayesian Modeling of Uncertainty in Low-Level Vision. Kluwer Academic Publishers: Boston, Massachusetts. Google Scholar
Szeliski, R. 1990. Fast surface interpolation using hierarchical basis functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(6):513–528. Google Scholar
Szeliski, R. 1996. Video mosaics for virtual environments. IEEE Computer Graphics and Applications, 16(2):22–30. Google Scholar
Szeliski, R. and Kang, S. B. 1994. Recovering 3D shape and motion from image streams using nonlinear least squares. Journal of Visual Communication and Image Representation, 5(1):10–28. Google Scholar
Szeliski, R. and Kang, S. B. 1995. Direct methods for visual scene reconstruction. In IEEE Workshop on Representations of Visual Scenes, Cambridge, Massachusetts, pp. 26–33.
Szeliski, R. and Shum, H.-Y. 1996. Motion estimation with quadtree splines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(12):1199–1210. Google Scholar
Szeliski, R., Kang, S. B., and Shum, H.-Y. 1995. A parallel feature tracker for extended image sequences. In IEEE International Symposium on Computer Vision, Coral Gables, Florida, pp. 241–246. Google Scholar
Terzopoulos, D. 1986. Regularization of inverse visual problems involving discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(4):413–424. Google Scholar
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. Google Scholar
Witkin, A., Terzopoulos, D., and Kass, M. 1987. Signal matching through scale space. International Journal of Computer Vision, 1:133–144. Google Scholar
Wolberg, G. 1990. Digital Image Warping. IEEE Computer Society Press: Los Alamitos, California. Google Scholar
Xu, G., Tsuji, S., and Asada, M. 1987. Amotion stereo method based on coarse-to-fine control strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9(2):332–336. Google Scholar
Zheng, Q. and Chellappa, R. 1992. Automatic feature point extraction and tracking in image sequences for arbitrary camera motion. Technical Report CAR-TR-628, Computer Vision Laboratory, Center for Automation Research, University of Maryland.