Image segmentation by flexible models based on robust regularized networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2352, Issue , 2002, Pages 621-634

  Rivera, Mariano ^a,b   Gee, James ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS   (Mexico)

Author keywords

Edge preserving regularization; Restoration; Segmentation

Indexed keywords

APPROXIMATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTER VISION; IMAGE ENHANCEMENT; IMAGE RECONSTRUCTION; ITERATIVE METHODS; RESTORATION;

CLASSIFICATION ERRORS; EDGE-PRESERVING REGULARIZATION; FLEXIBLE MODEL; IMAGE GRADIENTS; ITERATIVE ALGORITHM; LOCAL MINIMIZATION ALGORITHM; LOCAL MINIMIZER; RESTORATION PROBLEMS;

IMAGE SEGMENTATION;

EID: 84949993085     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47977-5_41     Document Type: Conference Paper

References (28)

1. Li, S.Z.: Markov Random Field Modeling in Image Analysis, Springer-Verlag, New York (2001)
2. Kirkpatrick, S., Gellatt, C. D., Vecchi, M.P.: Optimization by simulated annealing, Science, 220 (1983) 671–680
3. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell., 6 (1984) 721–741
4. Besag, J.: On the statistics analysis of dirty pictures (with discussions), Journal of the Royal Statistical Society, Series B, 48 (1986) 259–302
5. Peterson C., Soderberg, B.: A new method for mapping optimization problems onto neural networks, International Journal of Neural Systems, 1 (1989) 3–22
6. Geiger D., Girosi, F.: Parallel and deterministic algorithms for MRFs: Surface reconstruction and Integration, IEEE Trans. Pattern Anal. Machine Intell., 12 (1991) 401–412
7. Zhang, J.: Mean field theory in EM procedures for MRFs,” IEEE Trans. Signal Processing, 40 (1992) 2570–2583
8. Heitz, F., Perez, P., Bouthermy, P.: Multiscale minimization of global energy functions in some visual recovery problems, Computer Vision, Graphics, and Image Processing: Image Understanding, 59 (1994) 125–136
9. Faugeras, O., Price, K.: Semantic description of aerial images using stochastic labeling, IEEE Trans. Pattern Anal. Machine Intell., 3 (1981) 638–642
10. Hummel, R.A., Zucker, S.W.: On the foundations of relaxation labeling process, IEEE Trans. Pattern Anal. Machine Intell., 5 (1983) 267–286
11. Li, S.Z., MAP image restoration and segmentation by constrained optimization, IEEE Trans. Image Processing, 9 (1998) 1134–1138
12. Marroquin, J.L., Velazco, F.A., Rivera, M., Nakamura, M.: Gauss–Markov Measure Field Models for low–level vision, IEEE Trans. Pattern Anal. Machine Intell., 23 (2001) 337–348
13. Darrell, T.J., Pentland, A.P.: Cooperative robust estimation using layers of support, IEEE Trans. Pattern Anal. Machine Intell., 17 (1995) 474–487
14. Black, M.J., Fleet D.J., Yacoob, Y.: Robustly estimating changes in image appearance, Comput. Vision Image Understand. 78 (2000) 8–31
15. Marroquin, J.L., Botello, S., Calderon F., Vemuri, B.C.: MPM-MAP for image segmentation, In Procc. ICPR 2000, Barcelona Spain, 4 (2000) 300–310
16. Samson, C., Blanc-Feraud, L., Aubert, G., Zerubia, J.: A variational model for image classification and restoration, IEEE Trans. Pattern Anal. and Machine Intell., 22 (2000) 460-472
17. Girosi, F., Jones, M., Poggio, T.: Regularization theory and neural networks architectures, Neural Computing, 7 (1995) 219–269
18. Rivera, M., Marroquin, J.L.: Efficient Half-Quadratic Regularization withGran ularity Control, Technical Report: 05.06.2001, I-01-07 (CC), CIMAT, Mexico (2001)
19. Geman, S., McClure, D.E.: Bayesian image analysis methods: An application to single photon emission tomography, Proc. Statistical Computation Section, Amer. Statistical Assoc., Washington, DC, (1985) 12–18
20. Geman, D., Reynolds, G.: Constrained restoration and the recovery of discontinuities, IEEE Trans. Image Processing, 14 (1992) 367–383
21. Geman, D., Yang, C.: Nonlinear image recovery withh alf-quadratic regularization, IEEE Trans. Image Processing, 4 (1995) 932–946
22. Black, M.J., Rangarajan, A.: Unification of line process, outlier rejection, and robust statistics withapplication in early vision,” Int. J. Comput. Vis., 19, (1996) 57–92
23. Charbonnier, P., Blanc-Féraud, L., Aubert, G., Barlaud, G.: Deterministic edge– preserving regularization in computer imaging, IEEE Trans. Image Processing, 6 (1997) 298-311
24. Rivera, M., Marroquin, J.L.: The adaptive rest condition spring model: an edge preserving regularization techinque, in Proc.Interational Conference on Image Processing, IEEE-ICIP 2000, Vol. II, Vancouver, BC, Canada (2000) 805–807
25. Rivera, M., Marroquin, J.L.: Adaptive Rest Condition Potentilas: Second Order Edge-Preserving Regularization, In Proc. European Conference on Computer Vision, ECCV 2002, Springer-Verlag, Copenhagen, Denmark, May (2002)
26. Girosi, F., Poggio, T., Caprile, B.: Extensions of a theory of networks for approximation of and learning: outliers and negative examples,” In R. Lippmann, J. Moody and D. Tourezky, editors, Advances in Neural information processing systems 3, San Mateo, CA, Cambridge University Press (1991)
27. Girosi, F., Jones, M., Poggio, T.: Priors, stabilizers and basis functions: From regularization to radial, tensor and additive splines, MIT AI Lab Memo, No. 1430, June (1993)
28. Weickert, J.: Efficient image segmentation using partial differentiable equations and morphology, Pattern Recognition, 34 (2001) 1813-1824.