Hierarchical Image Segmentation - Part I: Detection of Regular Curves in a Vector Graph

International Journal of Computer Vision

Volumn 27, Issue 1, 1998, Pages 71-100

  Casadei, Stefano ^a   Mitter, Sanjoy ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL GEOMETRY; EDGE DETECTION; PROBLEM SOLVING; VECTORS; GRAPH THEORY; IMAGE RECONSTRUCTION; THEOREM PROVING;

LINEAR TIME COMPLEXITY;

IMAGE SEGMENTATION;

COST FUNCTIONS; CURVE DETECTION; VECTOR GRAPH;

EID: 0032022197     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007905813513     Document Type: Article

References (41)

1. Bienenstock, E. and Geman, S. 1995. Compositionality in neural systems. In The Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), MIT Press.
2. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:679-698.
3. Casadei, S. 1995. Model based detection of curves in images. Technical Report LIDS-TH-2306, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology.
4. Casadei, S. and Mitter, S.K. 1996a. A hierarchical approach to high resolution edge contour reconstruction. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition.
5. Casadei, S. and Mitter, S.K. 1996b. Hierarchical curve reconstruction. Part 1: Bifurcation analysis and recovery of smooth curves. In Proc. of the Fourth European Conf. on Computer Vision.
6. Caselles, V., Kimmel, R., and Sapiro, G. 1995. Geodesic active contours. In Proc. of the Int. Conf. of Computer Vision, pp. 694-699.
7. Cohen, L. and Kimmel, R. 1996. Global minimum for active contour models: A minimum path approach. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 666-673.
8. David, C. and Zucker, S. 1990. Potentials, valleys, and dynamic global coverings. International Journal of Computer Vision, 5(3):219-238.
9. Deriche, R. and Blaszka, T. 1993. Recovering and characterizing image features using an efficient model based approach. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition.
10. Dolan, J. and Riseman, E. 1992. Computing curvilinear structure by token-based grouping. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition.
11. Elder, J. and Zucker, S. 1996a. Local scale control for edge detection and blur estimation. In Proc. of the Fourth European Conf. on Computer Vision, pp. II:57-69.
12. Elder, J.H. and Zucker, S.W. 1996b. Computing contour closure. In Proc. of the Fourth European Conf. on Computer Vision, pp. 399-411.
13. Geiger, D. and Kumaran, K. 1996. Visual organization of illusory surfaces. In Proc. of the Fourth European Conf. on Computer Vision, pp. I:413-424.
14. Geiger, D., Kumaran, K., and Parida, L. 1996. Visual organization for figure/ground separation. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 155-160.
15. Geman, S. and Geman, D. 1984. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721-741.
16. Hancock, E. and Kittler, J. 1990. Edge-labeling using dictionarybased relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:165-181.
17. Haralick, R. 1984. Digital step edges from zero crossing of second directional derivatives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:58-68.
18. Iverson, L. and Zucker, S. 1995. Logical/linear operators for image curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(10):982-996.
19. Kanizsa, G. 1979. Organization in Vision: Essays on Gestalt Perception. Praeger: NY.
20. Kass, M., Witkin, A., and Terzopoulos, D. 1988. Snakes: Active contour models. International Journal of Computer Vision, 1:321-331.
21. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., and Yezzi, A. 1995. Gradient flows and geometric active contour models. In Proc. of the Int. Conf. of Computer Vision, pp. 810-815.
22. Lowe, D. 1985. Perceptual Organization and Visual Recognition. Kluwer.
23. Marroquin, J., Mitter, S.K., and Poggio, T. 1987. Probabilistic solution of ill-posed problems in computational vision. Journal of American Statistical Ass., 82(397):76-89.
24. Mohan, R. and Nevatia, R. 1992. Perceptual organization for scene segmentation and description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14.
25. Mumford, D. and Shah, J. 1989. Optimal approximations of piecewise smooth functions and associated variational problems. Comm. in Pure and Appl. Math., 42:577-685.
26. Nayar, S., Baker, S., and Murase, H. 1996. Parametric feature detection. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 471-477.
27. Nitzberg, M. and Mumford, D. 1990. The 2.1-d sketch. In Proc. of the Third Int. Conf. of Computer Vision, pp. 138-144.
28. Nitzberg, M., Mumford, D., and Shiota, T. 1993. Filtering, segmentation, and depth, vol. 662 of Lecture Notes in Computer Science. Springer-Verlag.
29. Parent, P. and Zucker, S. 1989. Trace inference, curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11.
30. Perona, P. 1992. Steerable-scalable kernels for edge detection and junction analysis. In Proc. of the Second European Conf. on Computer Vision, pp. 3-18.
31. Perona, P. and Malik, J. 1990. Detecting and localizing edges composed of steps, peaks and roofs. In Proc. of the Third Int. Conf. of Computer Vision, Osaka, pp. 52-57.
32. Richardson, T.J. and Mitter, S.K. 1994. Approximation, computation, and distorsion in the variational formulation. In Geometry-Driven Diffusion in Computer Vision. B. Ter Haar Romeny (Ed.), Kluwer.
33. Rohr, K. 1992. Recognizing corners by fitting parametric models. International Journal of Computer Vision, 9(3).
34. Rosenthaler, L., Heitger, F., Kubler, O., and von der Heydt, R. 1992. Detection of general edges and keypoints. In Proc. of the Second European Conf. on Computer Vision.
35. Sarkar, S. and Boyer, K. 1993. Perceptual organization in computer vision: A review and a proposal for a classificatory structure. IEEE Trans. Systems, Man, and Cybernetics, 23:382-399.
36. Sha'ashua, A. and Ullman, S. 1988. Structural saliency: The detection of globally salient structures using a locally connected network. In Proc. of the Second Int. Conf. of Computer Vision, pp. 321-327.
37. Shah, J. 1996. A common framework for curve evolution, segmentation and anisotropic diffusion. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 136-142.
38. Subirana-Vilanova, J.B. and Sung, K.K. 1992. Perceptual organization without edges. In Image Understanding Workshop.
39. Williams, L. and Jacobs, D. 1995. Stochastic completion fields: A neural model of illusory contour shape and salience. In Proc. of the Int. Conf. of Computer Vision, pp. 408-415.
40. Zhu, S., Lee, T., and Yuille, A. 1995. Region competition: Unifying snakes, region growing, and bayes/mdl for multi-band image segmentation. Technical Report CICS-P-454, Center for Intelligent Control Systems.
41. Zucker, S.W., David, C., Dobbins, A., and Iverson, L. 1988. The organization of curve detection: Coarse tangent fields and fine spline coverings. In Proc. of the Second Int. Conf. of Computer Vision.