Boundary Finding with Parametrically Deformable Models

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 14, Issue 11, 1992, Pages 1061-1075

  Staib, Lawrence H ^a   Duncan, James S ^a  

^a YALE UNIVERSITY   (United States)

Author keywords

2 D representation and recognition; Boundary finding; deformable models; Fourier descriptors; parametric models; probabilistic models

Indexed keywords

OPTIMIZATION; PROBABILITY;

BOUNDARY FINDING; DEFORMABLE MODEL; GLOBAL SHAPE INFORMATION; IMAGE SEGMENTATION; MAXIMUM A POSTERIORI OBJECTIVE FUNCTIONS; PARAMETRICALLY DEOFRMABLE MODELS;

IMAGE ANALYSIS;

EID: 0026943804     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.166621     Document Type: Article

References (36)

1. I. E. Abdou and W. K. Pratt. “Quantitative design and evaluation of enhancement/thresholding edge detectors.” Proc. IEEE, vol. 67, no. 5, pp. 753–763, May 1979.
2. R. Bajcsy and S. Kovacic. “Multiresolution elastic matching.” Comput. Vision Graphics Image Processing, vol. 46, pp. 1–21, 1989.
3. R. Bajcsy and F. Solina, “Three dimensional object representation revisited”, in Proc. Int. Conf.Comput. Vision, June 1987, pp. 231–239.
4. D. H. Ballard. “Generalizing the Hough transform to detect arbitrary shapes”, Patt. Recogn., vol. 13, no. 2, pp. 111–122, 1981.
5. D. H. Ballard and C. M. Brown, Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1982.
6. P. J. Besl. “Geometric modeling and computer vision”, Proc. IEEE, vol. 76, no. 8, pp. 936–958, Aug. 1988.
7. D. J. Burr, “A dynamic model for image registration”, Comput. Graphics Image Processing, vol. 15, pp. 102–112, 1981.
8. Y. Chow, U. Grenander, and D. M. Keenan, “Hands: A pattern theoretic study of biological shapes.” Monograph, Div. Applied Math., Brown Univ., Providence, RI, 1989.
9. D. B. Cooper, “Maximum likelihood estimation of Markov-process blob boundaries in noisy images.” IEEE Trans. Patt. Anal. Machine Intell, vol. PAMI-1, no. 4, pp. 372–384, Oct. 1979.
10. R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures”, Commun. ACM, vol. 15, no. 1, pp. 11–15, Jan. 1972.
11. J. S. Duncan, R. L. Owen, L. H. Staib, and P. Anandan, “Measurement of nonrigid motion in images using contour shape descriptors”, in Proc. IEEE Conf. Comput. Vision Patt. Recogn., June 1991, pp. 318–324.
12. M. A. Fischler and R. A. Elschlager, “The representation and matching of pictorial structures.” IEEE Trans. Comput., vol. C-22, pp. 67–92, Jan. 1973.
13. C. R. Giardina and F. P. Kuhl. “Accuracy of curve approximation by harmonically related vectors with elliptical loci”, Comput. Graphics Image Processing, vol. 6, pp. 277–285, 1977.
14. G. H. Granlund, “Fourier preprocessing for hand print character recognition”, IEEE Trans. Comput., vol. C-21, pp. 195–201, Feb. 1972.
15. C. W. K. Gritton and E. A. Parrish, Jr. “Boundary location from an initial plan: The bead chain algorithm”, IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-5, no. 1, pp. 8–13, Jan. 1983.
16. B. K. P. Horn. Robot Vision. Cambridge, MA: MIT Press, 1986.
17. B. K. P. Horn and E. J. Weldon. “Filtering closed curves”, IEEE Trans. Patt. Anal. Machine Intell, vol. PAMI-8, no. 5, pp. 665–668, Aug. 1986.
18. M. Kass, A. Witkin, and D. Terzopoulos. “Snakes: Active contour models”, Int. J. Comput. Vision, vol. 1, no. 4, pp. 321–331, 1988.
19. F. P. Kuhl and C. R. Giardina, “Elliptic Fourier features of a closed contour”, Comput. Graphics Image Processing, vol. 18, pp. 236–258, 1982.
20. C. Lin and C. Hwang, “New forms of shape invariants from elliptic Fourier descriptors”, Patt. Recogn., vol. 20, no. 5, pp. 535–545, 1987.
21. D. Lowe, “Three dimensional object recognition from single two-dimensional images”, Artificial Intell, vol. 31, no. 3, pp. 355–396, 1987.
22. D. Lowe, “Organization of smooth image curves at multiple scales”, in Proc. Int. Conf. Comput. Vision, Dec. 1988, pp. 558–567.
23. A. Martelli, “An application of heuristic search methods to edge and contour detection”, Commun. ACM, vol. 19, no. 2, pp. 73–83, Feb. 1976.
24. O. R. Mitchell and T. A. Grogan, “Global and partial shape discrimination for computer vision”, Optical Eng., vol. 23, no. 5, pp. 484–491, 1984.
25. R. Mohan and R. Nevatia, “Segmentation and description based on perceptual organization”, in Proc. IEEE Conf. Comput. Vision Patt. Recogn., June 1989, pp. 333–34.
26. U. Montanari, “On the optimal detection of curves in noisy pictures”, Commun. ACM, vol. 14, no. 5, pp. 335–345, May 1971.
27. E. Persoon and K. Fu, “Shape discrimination using Fourier descriptors”, IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-8, no. 3, pp. 388–397, May 1986.
28. D. A. Pierre, Optimization Theory with Applications. New York: Dover, 1986.
29. T. Poggio and V. Torre, “Ill-posed problems and regularization analysis in early vision”, Memo 773, MIT AI Lab., Apr. 1984.
30. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes. Cambridge, UK: Cambridge University Press, 1986.
31. A. Rosenfeld and A. C. Kak, Digital Picture Processing (2nd Ed.). Orlando, FL: Academic, 1982, vol. 2.
32. C. E. Shannon, “A mathematical theory of communication”, Bell Syst. Tech. J., vol. 27, pp. 379–423 and 623–656, 1948.
33. L. H. Staib, “Parametrically deformable contour models for image analysis”, Ph.D. thesis, Yale Univ., New Haven, CT, 1990.
34. B. Widrow, “The “rubber mask” technique-I and II”, Patt. Recogn., vol. 5, pp. 175–211, 1973.
35. A. Witkin, D. Terzopoulos, and M. Kass, “Signal matching through scale space”, Int. J. Comput. Vision, vol. 1, pp. 133–144, 1987.
36. A. L. Yuille, D. S. Cohen, and P. W. Hallinan, “Feature extraction from faces using deformable templates”, in Proc. IEEE Conf Comput. Vision Patt. Recogn., June 1989, pp. 104–109.