Instantiating Deformable Models with a Neural Net

Computer Vision and Image Understanding

Volumn 68, Issue 1, 1997, Pages 120-126

  Williams, Christopher K I ^a,b   Revow, Michael ^a   Hinton, Geoffrey E ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; ITERATIVE METHODS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY;

DEFORMABLE MODELS; HANDWRITTEN DIGIT RECOGNITION;

CHARACTER RECOGNITION;

EID: 0031257313     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1006/cviu.1997.0540     Document Type: Article

References (30)

1. R. Bajcsy, R. Lieberson, and M. Reivich, A computerized system for the elastic matching of deformed radiographic images to idealized atlas images, J. Comput. Assist. Tomogr. 7(4), 1983, 618-625.
2. D. H. Ballard, Generalizing the Hough transfrom to detect arbitrary shapes, Pattern Recognit. 13(2), 1981, 111-122.
3. J. S. Beis and D. G. Lowe, Learning indexing functions for 3-D model-based object recognition, in AAAI Fall 1993 Symposium on Machine Learning in Computer Vision, 1993, pp. 50-54. [Proceedings available as AAAI Tech Report FSS-93-04.]
4. D. Beymer, A. Shashua, and T. Poggio, Example Based Image Analysis and Synthesis, AI Memo 1431, AI Laboratory, MIT, 1993.
5. A. Blake, R. Curwen, and A. Zisserman, A Framework for Spatiotemporal Control in the Tracking of Visual Contours. International Journal of Computer Vision, 11(2), 1993, 127-145.
6. J.S. Bridle, Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, in NATO ASI Series on Systems and Computer Science (F. Fougelman-Soulie and J. Herault, Eds.), Springer-Verlag, New York/Berlin, 1990.
7. D. Broomhead and D. Lowe, Multivariable functional interpolation and adaptive networks, Complex Systems 2, 1988, 321-355.
8. D. J. Burr, A dynamic model for image registration. Comput. Graphics Image Process. 15, 1981, 102-112.
9. D. J. Burr, Elastic matching of line drawings. IEEE Trans. Pattern Analysis and Machine Intelligence 3(6), 1981, 708-713.
10. M. A. Fischler and R. A. Elschlager, The representation and matching of pictorial structures. IEEE Trans. Computers C-22(1), 1973, 67-92.
11. U. Grenander, Y. Chow, and D. M. Keenan, Hands: A Pattern Theoretic Study of Biological Shapes, Springer-Verlag, New York/Berlin, 1991.
12. W. E. L. Grimson, Object Recognition by Computer, MIT Press, Cambridge, MA, 1990.
13. G. E. Hinton, C. K. I. Williams, and M. D. Revow, Adaptive elastic models for hand-printed character recognition, in Advances in Neural Information Processing Systems 4 (J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds.), Morgan Kauffmann, San Mateo, CA, 1992.
14. G. E. Hinton, C. K. I. Williams, and M. D. Revow, Combining two methods of recognizing hand-printed digits. In I. Aleksander and J. Taylor, editors, Artificial Neural Networks 2. Elsevier Science Publishers, 1992.
15. B. K. P. Horn, Robot Vision. MIT Press, Cambridge, MA, 1986.
16. D. J. Hunt, L. W. Nolte, and W. H. Ruedger, Performance of the Hough Transform and its Relationship to Statistical Signal Detection Theory. Computer Vision, Graphics and Image Processing 43, 1988, 221-238.
17. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, Adaptive mixtures of local experts. Neural Computation 3(1), 1991.
18. M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models. In Proceedings of the First International Conference on Computer Vision, Washington, D. C., 1987. IEEE Computer Society Press.
19. Y. Lamdan and H. J. Wolfson, Geometric Hashing: A General and Efficient Model-Based Recognition Scheme. In Proceedings of the Second International Conference on Computer Vision. IEEE, 1988.
20. A. Lanitis, C. J. Taylor, and T. F. Cootes, A generic system for classifying variable objects using flexible template matching. In Proceedings of the British Machine Vision Conference (J. Illingworth, Ed.), Vol. 1, pp. 329-338, BMVA Press, 1993.
21. D. G. Lowe, Perceptual Organization and Visual Recognition, Kluwer, Boston, 1985.
22. T. Poggio and F. Girosi, Networks for approximation and learning. Proceedings of IEEE 78, 1990, 1481-1497.
23. M. D. Revow, C. K. I. Williams, and G. E. Hinton, Using generative models for handwritten digit recognition, 1994, IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 1996, 592-606.
24. R. S. Stephens, Probabilistic approach to the Hough transfrom, Image Vision Comput. 9(1), 1991, 66-71.
25. B. Widrow, The 'rubber-mask' technique-I. Pattern Measurement and Analysis. Pattern Recognition 5, 1973, 175-197.
26. C. K. I. Williams, Combining Deformable Models and Neural Networks for Handprinted Digit Recognition, Ph.D. thesis, Dept. of Computer Science, University of Toronto, 1994.
27. C. K. I Williams, M. D. Revow, and G. E. Hinton, Using a neural net to instantiate a deformable model, In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems, Vol. 7. MIT Press, 1995.
28. A. L. Yuille, Deformable templates for face recognition. Journal of Cognitive Neuroscience 3(1), 1991, 59-70.
29. R. S. Zemel and G. E. Hinton, Discovering viewpoint-invariant relationships that characterize objects. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances In Neural Information Processing Systems 3, pp. 299-305. Morgan Kaufmann Publishers, 1991.
30. R. S. Zemel, M. C. Mozer, and G. E. Hinton, TRAFFIC: Recognizing objects using hierarchical reference frame transformations. In D. S. Touretzky, editor, Neural Information Processing Systems, Vol. 2, pp. 266-273. Morgan Kaufmann, San Mateo, CA, 1990.