Information-theoretic active polygons for unsupervised texture segmentation

International Journal of Computer Vision

Volumn 62, Issue 3, 2005, Pages 199-220

  Unal, Gozde ^a   Yezzi, Anthony ^b   Krim, Hamid ^c  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c North Carolina State University   (United States)

Author keywords

Electrostatic regularizer; Information theoretic measure; Polygon evolution; Region based active contours; Texture segmentation; Unsupervised segmentation

Indexed keywords

ELECTROSTATIC REGULARIZER; INFORMATION THEORETIC MEASURE; OBJECT DELINEATION; POLYGON EVOLUTION; POLYGONAL APPROXIMATIONS; REGION-BASED ACTIVE CONTOURS; TEXTURE SEGMENTATION; UNSUPERVISED SEGMENTATION;

APPROXIMATION THEORY; COMPUTATIONAL GEOMETRY; COMPUTER VISION; DATA STORAGE EQUIPMENT; DATABASE SYSTEMS; IMAGE CODING; INFORMATION ANALYSIS; MATHEMATICAL MODELS; MULTIMEDIA SYSTEMS; PARTIAL DIFFERENTIAL EQUATIONS; PROBABILITY DENSITY FUNCTION;

IMAGE SEGMENTATION;

EID: 14844342382     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-005-4880-6     Document Type: Review

References (50)

1. Besag, J. 1973. Spatial interaction and the statistical analysis of lattice systems. J. Royal Stat. Soc. B, 36:192-236.
2. Blake, A. and Isard, M. 1998. Active Contours. Springer Verlag: London, Great Britain.
3. Blake, A. and Zisserman, A. 1987. Visual Reconstruction. MIT Press: Cambridge, MA.
4. Bruckstein, A., Sapiro, G., and Shaked, D. 1995. Evolutions of planar polygons. Int. J. Pattern Recognition and Artificial Intell., 9(6):991-1014.
5. Caselles, V., Catte, F., Coll, T., and Dibos, F. 1993. A geometric model for active contours in image processing. Numerische Mathematik, 66:1-31.
6. Caselles, V., Kimmel, R., and Sapiro, G. 1995. Geodesic active contours. In ICCV95, pp. 694-699.
7. Chakraborty, A., Staib, L., and Duncan, J. 1996. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans. on Medical Imaging, 15(6):859-870.
8. Chan, T.F. and Vese, L.A. 1999. An active contour model without edges. In Int. Conf. Scale-Space Theories in Computer Vision, pp. 141-151.
9. Chan, T. and Vese, L. 2001. A level set algorithm for minimizing the Mumford-Shah functional in image processing. In Proc. IEEE Workshop on Variational and Level Set Methods.
10. Chesnaud, C., Refregier, P., and Boulet, V. 1999. Statistical region snake-based segmentation adapted to different physical noise models. PAMI, 21:1145-1156.
11. Cover, T.M. and Thomas, J.A. 1991. Elements of Information Theory. Wiley: New York.
12. Cremers, D., Tischhäuser, F., Weickert, J., and Schnörr, C. 2002. Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional. Int. J. Computer Vision, 50(3):295-313.
13. Cross, G. and Jain, A.K. 1983. Markov random field texture models. IEEE Trans. Pattern Analysis, and Machine Intelligence, 5:25-39.
14. Dunham, J.G. 1986. Optimum uniform piecewise linear approximation of planar curves. IEEE Trans. Pattern Analysis, and Machine Intelligence, 8:67-75.
15. Figueiredo, M.A.T., Leitao, J.M.N., and Jain, A.K. 2000. Unsupervised contour representation and estimation using b-splines and a minimum description length criterion. IEEE Trans. Image Process., 9(6):1075-1087.
16. Freeman, H. 1978. Shape description via the use of critical points. Pattern Recognition, 10:159-166.
17. Geman, S. and Geman, D. 1984. Stochastic relaxation, gibbs distribution, and the bayesian restoration of images. IEEE Trans. Pattern Analysis, and Machine Intelligence, 6:721-741.
18. Gomez-Lopera, J.F., Martinez-Aroza, J., Robles-Perez, A.M., and Roman-Roldan, R. 2000. An analysis of edge detection by using the jensen-shannon divergence. J. Mathematical Imaging and Vision, 13:35-56.
19. Grenander, U. and Srivastava, A. 2001. Probability models for clutter in natural images. IEEE Trans. Pattern Analysis, and Machine Intelligence, 23(4):424-429.
20. He, Y., Hamza, A.B., and Krim, H. 2003. A generalized divergence measure for robust image registration. IEEE Trans. Signal Process., 51.
21. Hyvarinen, A. 1997. New approximations of differential entropy for independent component analysis and projection pursuit. Tech. Rep., Helsinki University of Technology.
22. Hyvarinen, A. and Oja, E. 2000. Independent component analysis: Algorithms and applications. Neural Networks, 13(4-5):411-430.
23. Imai, H. and Iri, M. 1986. Computational-geometric methods for polygonal approximations of a curve. Computer Vision, Graphics, and Image Processing, 36:31-41.
24. Jain, A.K. and Farrokhnia, F. 1991. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 24(12):1167-1186.
25. Jaynes, E.T. 1963. Information Theory and Statistical Mechanics. K. Ford (Ed.), New York, p. 181.
26. Kass, M., Witkin, A., and Terzopoulos, D. 1988. Snakes: Active contour models. Int. J. Computer Vision, 1(4):321-331.
27. Kendall, M. and Stuart, A. 1958. The Advanced Theory of Statistics. Charles Griffin & Company.
28. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., and Yezzi, A. 1995. Gradient flows and geometric active contours. In Proc. Int. Conf. on Computer Vision, pp. 810-815.
29. Koch, M.W. and Kashyap, R.L. 1987. Using polygons to recognize and locate partially occluded objects. IEEE Trans. Pattern Analysis, and Machine Intelligence, 9(4):483-494.
30. Kurozumi, Y. and Davis, W.A. 1982. Polygonal approximation by the minimax method. Comput. Graph., Image Proc., 248-264.
31. Leclerc, Y. 1989. Constructing stable descriptions for image partitioning. Int. J. Computer Vision, 3:73-102.
32. Lin, J. 1991. Divergence measures based on the Shannon entropy. IEEE Trans. Information Theory, 37(1):145-151.
33. Malladi, R., Sethian, J.A., and Vemuri, B.C. 1995. Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Analysis, and Machine Intelligence, 17(2):158-173.
34. Manjunath, B.S. and Chellappa, R. 1991. Unsupervised texture segmentation using Markov Random Field models. IEEE Trans. Pattern Analysis, and Machine Intelligence, 13(5):478-482.
35. Mumford, D. and Shah, J. 1985. Boundary detection by minimizing functionals. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Fransisco.
36. O'Connell, K.J. 1997. Object-adaptive vertex-based shape coding method. IEEE Trans. Circuits and Systems on Video Technology, 7:251-255.
37. Osher, S. and Sethian, J.A. 1988. Fronts propagating with curvature dependent speed: Algorithms based on the Hamilton-Jacobi formulation. J. Computational Physics, 49:12-49.
38. Paragios, N. and Deriche, R. 2002. Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Computer Vision, 46(3):223-247.
39. Ronfard, R. 1994. Region-based strategies for active contour models. In IJCV, 13:229-251.
40. Sethian, J.A. 1999. Level Set Methods and Fast Marching Methods. Cambridge University Press.
41. Siddiqi, K., Lauziere, Y.B., Tannenbaum, A., and Zucker, S.W. 1998. Area and length minimizing flows for shape segmentation. IEEE Trans. Image Process., 7(3):433-443.
42. Simoncelli, E.P., Freeman, W.T., Adelson, E. H., and Heeger, D.J. 1992. Shiftable multiscale transforms. IEEE Trans. Information Theory, 38(2):587-607.
43. Tsai, A., Yezzi, A., and Willsky, A. 2001. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process., 10(8):1169-1186.
44. Ulaby, F.T. 1997. Fundamentals of Applied Electromagnetics. Prentice Hall, New Jersey.
45. Unal, G., Krim, H., and Yezzi, A. 2002. Active polygons for object tracking. In IEEE 1st Int. Conf. 3D Data Processing and Visualization and Transmission, Padova, Italy.
46. Yezzi, A., Tsai, A., and Willsky, A. 1999. A statistical approach to snakes for bimodal and trimodal imagery. In Proc. Int. Conf. on Computer Vision, pp. 898-903.
47. Yezzi, A., Tsai, A., and Willsky, A. 2002. A fully global approach to image segmentation via coupled curve evolution equations. Journal of Visual Communication and Image Representation, 13(1):195-216.
48. Yun, B.-J., Lee, S.-W., and Kim, S.-D. 2001. Vertex adjustment method using geometric constraint for polygon-based shape coding. Electronics Letters, 37:754-755.
49. Zhu, S. and Yuille, A. 1996. "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation." IEEE Trans. Pattern Analysis, and Machine Intelligence, 18:884-900.
50. Zhu, S.C., Wu, Y., and Mumford, D. 1998. Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling. Int. J. Computer Vision, 27(2):107-126.