Object Detection and Localization by Dynamic Template Warping

• Baker, H.H. and Binford, T.O. 1981. Depth from edge and intensity based stereo. In Proc. 7th IJCAI, pp. 631–636.

• Ballard, D. and Brown, C. 1982. In Computer Vision. Prentice Hall.

• Barrow, H.G. 1976. Interactive aids for cartography and photo interpretation. SRI Tech. Report, SRI International.

• Betke, M. and Makris, N. 1995. Fast object recognition in noisy images using simulated annealing. In Proc. Int. Conf. on Computer Vision, pp. 523–530.

• Beymer, D. 1993. Face recognition under varying pose. AI Memo 1461, Artificial Intelligence Lab at MIT, Cambridge, MA.

  Google Scholar 

• Brunelli, R. and Poggio, T. 1993. Face recognition: Features versus templates. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(10):1042–1052.

  Google Scholar 

• Cootes, T.F., Taylor, C.J., Lanitis, A., Cooper D.H., Graham, J. 1993. Building and using flexible models incorporating gray level information. In Proc. Int. Conf. on Computer Vision, Berlin, pp. 242–246.

• Corman, T., Leiserson, C., and Rivest, R. 1990. Introduction to Algorithms. McGraw Hill.

• Cyberware Incorporated Monterey, CA.

• Forney, G.D. 1973. The Viterbi algorithm. In Proceedings IEEE, Vol. 61, pp. 268–278.

  Google Scholar 

• Hornegger, J. 1995. Statistical learning, localization and identification of objects. In Proc. Int. Conf. on Computer Vision, Cambridge, MA, pp. 914–919.

• Huttenlocher, D.P. and Ullman, S. 1990. Recognizing solid objects by alignment with an image. Int. Journal of Computer Vision 5(2):195–212.

  Google Scholar 

• Huttenlocher, D.P., Lilien, R., and Olson, C. 1996. Object recognition using subspace methods. In Proc. European Conf. on Computer Vision, pp. 537–545.

• Mahmood, S.T.F. and Zhu, W. 1998. Image organization and retrieval using a flexible shape model. In Proc. of Content Based Access of Image and Video Libraries.

• Murase, H. and Nayar, S. 1995. Learning and recognition of 3-d objects from brightness images. AAAI Fall Symposium Series Working Notes, AAAI.

• Press, W. and Flannery, B. et al. 1990. Numerical Recipes in C. Cambridge University Press.

• Ohta, Y. and Kanade, T. 1985. Stereo by intra and inter-scanline search using dynamic programming. IEEE Trans. on Pattern Analysis and Machine Intelligence, 7(2).

• Pentland, A., Moghaddam, B., and Starner, T. 1994. View-based and modular eigenspaces for face recognition. In Proc. Computer Vision and Pattern Recognition, pp. 84–91.

• Romano, R., Beymer, D., and Poggio, T. 1996. Face verification for real time applications. ARPA, IU Workshop, Vol. 1.

• Rowley, H., Baluja, S., and Kanade, T. 1995. Human face detection in visual scenes. CMU-CS-95-158R, Carnegie Mellon University, Pittsburg, PA.

  Google Scholar 

• Rowley, H., Baluja, S., and Kanade, T. 1998. Rotation invariant neural-network based face detection. In Proc. Computer Vision and Pattern Recognition.

• Rucklidge, J. 1994. Locating objects using the hausdorff distance. Proc. Int. Conference on Computer Vision, pp. 457–464.

• Schneiderman, H. and Kanade, T. 1998. Probabilistic modeling of local appearance and spatial relationships for object recognition. In Proc. Computer Vision and Pattern Recognition.

• Sakoe, H. and Chiba, S. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., Vol. ASSP-26, pp. 43–49.

  Google Scholar 

• Shashua, A. and Ullman, S. 1988. Structural saliency: The detection of globally salient structures using a locally connected network. In Proc. Int. Conference on Computer Vision, pp. 321–327.

• Sinha, P. 1994. Object recognition via image invariants: Acase study. In Investigative Opthamology and Visual Science, Florida.

• Sung, K. and Poggio, T. 1994. Example based learning for viewbased human face detection. AI Memo 1521, MIT. Cambridge, MA.

  Google Scholar 

• Turk, M. and Pentland, A. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1).

• Ullman, S. and Basri, R. 1991. Recognition by linear combination of models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(10).

• Vaillant, R., Monrocq, C., and Le Cun, Y. 1994. Original approach for the localization of objects in images. IEEE Proc. on Vision, Image and Signal Processing, Vol. 141, No.4.

• Viola, P. and Wells, W.M. 1995. Alignment by maximization of mutual information. In Proc. Int. Conference on Computer Vision, Cambridge, MA.

• Viterbi, A.J. 1967. Error bounds for convolution codes and an asymptotically optimal decoding algorithm. IEEE Trans. on Information Theory, IT-13:260–269.

  Google Scholar 

• Yuille, A., Hallinan, P., and Cohen, D. 1992. Feature extraction from faces using deformable templates. Int. Journal of Computer Vision, 8(2):99–111.

  Google Scholar 

Download references