[1] Besag J., 1974, Spatial interaction and the statistical analysis of lattice systems, J.R.S.S.B, 36, 192-236. | MR | Zbl

[2] Besag J., 1986, On the statistical analysis of dirty pictures, J.R.S.S.B, 48, 259-302. | MR | Zbl

[3] Chalmond B., 1988, An iterative Gibbsian technique for reconstruction of M-ary images, to appear in Pattern Recognition.

[4] Craven P. and Wahaba G., 1979, Smoothing noisy data by spline functions, Num. Math, 31, 377-403. | Zbl | DOI

[5] Frigessi A. and Piccioni M., 1988, Parameter estimation for two-dimensional Ising fields corrupted by a noise, Quaderno n° 10, Inst. Appl. del Calculo, Roma. | MR | Zbl

[6] Geman S. et Geman D., 1984, Stochastic relaxation, Gibbs distributions and the bayesian restoration of Images, IEEE-PAMI, 6, 721-741. | Zbl | DOI

[7] Guyon X., 1987, Estimation d'un champ par pseudo-vraisemblance conditionnelle : étude asymptotique et application au cas markovien, in "Spatial processes and spatial time series analysis", Proc. 6 th Franco-Belgian Meeting of Statistics, Droesbecke F. editor, Bruxelles. | MR

[8] Hall P. and Titterington D.M, 1986, On some smoothing techniques in image restoration, J.R.S.S.B, 48, 330-343. | MR | Zbl

[9] Hilgers J.W, 1976, On the equivalence of regularization and certain reproducing kernel Hilbert space approximation for solving first kind problems, S.I.A.M J. Numer. Anal., 13, 172-174. | MR | Zbl | DOI

[10] Little R.J.A. and Rubin D.B., 1983, On jointly estimating parameters and missing values by maximising the complete data likelihood, Amer. Statist., 37, 218-220.

[11] Marroquin J., Mitter S. and Poggio T., 1987, Probabillistic solution of ill-posed problems in computational vision, JASA 79, n° 387, 584-589. | Zbl

[12] Porteous B.T., Greig D.M. and Seheult A.H., 1987, Exact M.A.P estimation for binary images, Preprint, University of Durham.

[13] Possolo A., 1986, Estimation of binary Markov Random Fields, Tech. report n° 77, Dept. of Stat., University of Washington.

[14] Silverman B.W., 1984, A fast efficient cross-validation method for smoothing parameter choice in spline regression, JASA 79, n° 387, 584-589. | MR | DOI

[15] Stone M., 1974, Cross-validatory choice and assessment of statistical prediction, J.R.S.S.B, 36, 111-147. | MR | Zbl

[16] Wahba G., 1977, Practical approximate solutions to linear operators equations when the data are noisy, S.I.A.M, J. Numer. Anal., 14, 651-667. | MR | Zbl | DOI

[17] Wahba G., 1982, Constrained regularization for ill-posed linear operator equations with application in meteorology and medecine, in "Statistical Design Theory and Related Topics : III, vol 2", Eds. S. GUPTA and J. D BERGER, N.Y., Academic Press. | MR | Zbl

[18] Wendelberger J., 1981, The computation of Laplacian smoothing splines with examples, Techn. report 648, Stat. Dept, Univ. of Wisconsin.

[19] Woodward W.A, Parr W.C, Schucany W.R and Lindsey H., 1984, A comparison of minimum distance and maximum likelihood estimation of a mixture proportion, J. Amer. Stat. Assoc., 79, 590-598. | MR | Zbl | DOI

[20] Yao J.F., 1988, Methodes bayesiennes en segmentation d'images (comparaison des methodes globales et contextuelles), in "Bayesian Statistics", Proc. 8th. Franco-Belgian Meeting of Statistics, Univ. of Louvain, Belgique.

[21] Younes L., 1988, Estimation and Annealing for Gibbsian Fields, Ann. Inst. Henri Poincare, Vol. 2, 269-294. | MR | Zbl | Numdam

[22] Younes L., 1988, Problèmes d'estimation paramétriques pour des champs de Gibbs Markoviens. Applications en traitement d'images, These de Docteur en Sciences, Universite Paris XI, Sept. 1988.

[I-1] Abend K., Harley T. J.,& Kanal L.N., 1965. Classification of binary random patterns. IEEE Trans. Inform. Th. 11, 538-544 | MR | Zbl | DOI

[I-2] Besag J.E., 1974. Spatial interaction and the statistical analysis of lattice systems. J. R. Statist. Soc. B-36, 192-236 | MR | Zbl

[I-3] Besag J.E., 1986. On the statistical analysis of dirty pictures. J. R. Statist. Soc. B-48, 259-302 | MR | Zbl

[I-4] Chalmond B., 1988. Image restauration using an estimed Markov model. Signal Processing 15, 115-129. | DOI

[I-5] Chalmond B., 1989. An iterative Gibbsian technique for reconstruction of M-ary images. Pattern Recognition 22 (6). | DOI

[I-6] Chalmond B., 1988. Reconstruction et restauration d'image : utilisation d'outils stochastiques. Thèse de Docteur en Sciences, Université de Paris - Sud.

[I-7] Chow C.K., 1962. A recognition method using neighbor dependence. IRE Trans. Electronic Computers 11, 683-690 | Zbl | DOI

[I-8] Cohen F.S. & Cooper D.B., 1987. Simple parallel hierarchical and relaxation algorithmes for segmenting noncausal markovian random fields. IEEE Trans. on Pattern Anal. and Machine Intell. 9, 195-219. | DOI

[I-9] Cross & Jain, 1983. Markov random field texture models. IEEE Trans. on Pattern Anal, and Machine Intell. 5 (1), 25-39. | DOI

[I-10] Derin H. & Elliot H., 1987. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. on Pattern Anal. and Machine Intell. 9, 39-55. | DOI

[I-11] Derin H., Elliot H., Christi R. & Geman D., 1984. Bayes smoothing algorithms for segmentation of binary images modelled by Markov random fields. IEEE Trans. on Pattern Anal. and Machine Intell. 6, 707-720. | Zbl | DOI

[I-12] Devijver P. A. & Dekesel M.M., 1987. Learning the parameters of a hidden Markov random field image model : a simple example. Dans Pattern Recognition : Theory and Applications (eds. DEVIJVER P.A. & KITTLER J.). NATO ASI Series F30, 141-163. Springer-Verlag.

[I-13] Devijver P.A., 1989. Real-time modeling of image sequences - based on Hidden markov mesh random field models. Manuscript M-307, Philips Research Laboratory Brussels.

[I-14] Dinten J. M., 1988. Tomographic reconstruction with a limited number of projections : regularization using a Markov model. Prépublication 88-42, Université de Paris-Sud et soumis à la publication.

[I-15] Dinten J. M., Guyon X. & Yao J. F. 1988. On the choice of the regularization parameter : the case of binary images in the Bayesian restoration framework Proc. AMS-IMS-SIAM Joint Conference on Spatial Statistics and Imaging, à paraître dans : Lectures Notes - Monograph Series of Inst. Math. Stat. (éditeur SERFLING R.).

[I-16] Geman S. & Geman D., 1984. Stochastic relaxation, Gibbs distributions and the bayésian restauration of images. IEEE Trans. on Pattern Anal. and Machine Intell. 6, 721-741. | Zbl | DOI

[I-17] Geman S. & Graffigne C., 1986. Markov random fields image models and their applications to computer vision. Proc. Int. Congr. of Math. 1986, AMS : Providence. | MR | Zbl

[I-18] Geman S., Geman D. & Graffigne C., 1986. Locating textures and objets boundries. Dans Pattern Recognition : Theory and Applications (eds. DEVIJVER P.A. & KITTLER J.). NATO ASI Series F30. Springer-Verlag.

[I-19] Geman S. & Mcclure, 1987. Statistical methods for tomographic image reconstruction. Dans Proceedings of the 46th Sessions of the International Statistical Institute, Bulletin of the ISI 52, 1-17. | MR

[I-20] Graffigne C., 1987. Experiments in textures analysis and segmentation. Ph. D. Dissertation, Brown University. | MR

[I-21] Guyon X. & Yao J.F., 1987. Analyse Discriminante Contextuelle. Dans Actes des 5-ièmes Journées Internationales en Analyse des Données et Informatiques (INRIA, France), North Holland | MR

[I-22] Hajek B., 1985. Cooling Schedules for optimal annealing. Mathematics of Operation Research 13 (2), 311-329. | MR | Zbl

[I-23] Haslett J., 1985. Maximum likelihood discriminant analysis on the plane using a Markovian model of spatial context. Pattern Recognition 18, 287-296. | Zbl | DOI

[I-24] Hassner M. & Sklansky J., 1980. The use of Markov random fields as models of texture. Computer Graphics and Image Processing 12, 357-370.

[I-25] Hjort N.L., 1985. Neighbourhood based classification of remotely sensed data based on geometric probability models. Technical Reporty 10, Stanford University.

[I-26] Khatri C.G., 1980. Quadratic forms in normal variables. Dans Handbook of Statistics (ed. KRISHNAIAH P.R.), Volume 1 443-469. North-Holland. | Zbl | DOI

[I-27] Kirpatrick S., Gelatt C.D. & Vecchi M.P., 1983. Optimization by simulated annealing. Science 220, 671-680. | MR | Zbl | DOI

[I-28] Kittler J. & Föglein J., 1984. Contextual classification of multispectral pixel data. Image and Vision Computing Journal 2, 13-39. | DOI

[I-29] Kotz S., Johanson N.L. & Boyd D.W., 1967. Series representations of quadratic forms in normal variables : I. Central case. Ann. Math. Statist. 38, 823-837. | MR | Zbl | DOI

[I-30] Kotz S., Johanson N.L. & Boyd D.W., 1967. Series representations of quadratic forms in normal variables : II. Non central case. Ann. Math. Statist. 38, 838-848 | MR | Zbl | DOI

[I-31] Mardia K. V., 1984. Spatial discrimination and classification maps. Commn. Stat. Theor. Math. 13 (18), 2184-2197. | MR | Zbl

[I-32] Marroquin J., Mitter S. & Poggio T., 1987. Probabilistic solution of ill-posed problems in computational vision. J. Amer. Stat. Ass. 82 (397), 76-89. | Zbl | DOI

[I-33] Owen A., 1984. A neighbourhood-based classifier for LANDSAT data. Can. J. Stat. 12, 191-200. | MR | Zbl | DOI

[I-34] Pickard D.K., 1977. A curious binary lattice process. J. Appl. Prob. 14, 717-731. | MR | Zbl | DOI

[I-35] Pickard D.K., 1980. Unilateral Markov fields. Adv. Appl. Prob. 12, 655-671. | MR | Zbl | DOI

[I-36] Greig D.M., Porteous B.T. & Seheult A.H, 1989. Exact Maximum A Posteriori estimation for binary images. Journal of the Royal Statistical Society B-51 (2), 271-279.

[I-37] Possolo A., 1986. Estimation of binary Markov Random Fields. Technical Report 77, Departement of Statitics, University of Washington.

[I-38] Ripley B.D., 1986. Statistics, images and pattern recognition. Can. J. Stat. 14, 83-111. | MR | Zbl | DOI

[I-39] Saebo H.V., Braten K., Hjort N.L., Llewellyn B. & Mohn E., 1985. Contextual classification of remotely sensed data : statistical methods and development of a system. Report 768, Norvegian Computing Center, Oslo.

[I-40] Swain P.H., Vardeman S.B. & Tilton J.C., 1981. Contextual classification of multispectral data. Pattern Recognition 13, 429-441. | DOI

[I-41] Switzer P., 1980. Extension of linear discriminant analysis for statistical classification of remoteley sensed satellite imagery. Math. Geol. 12, 367-376. | MR | DOI

[I-42] Van Laarhoven P.J.M. & Aarts E.H.L., 1987. Simulated annealing : theory and applications. D. Reidel Pub. Company. | MR | Zbl | DOI

[I-43] Yao J.F., 1989. Segmentation bayésienne d'image : comparaison des méthodes contextuelle et globale. Cahiers du Centre d'études de Recherche Opérationnelle 30 (4) (Université Libre de Bruxelles), 269-290. | Zbl

[I-44] Younes L., 1988. Estimation and Annealing for Gibbsian Fields. Annales de l'institut Henri Poincaré, 269-294. | MR | Zbl | Numdam

[I-45] Younes L., 1988. Problèmes d'estimation paramétriques pour des champs de Gibbs Markoviens. Applications en traitement d'images. Thèse de Docteur en Sciences, Université de Paris - Sud.

[I-46] Yu T.S & Fu K.S., 1983. Recursive contextual classification using a spatial stochastic model. Pattern Recognition 16, 89-108 | DOI

[I-47] Welch J.R. & Salter K.G., 1971. A context algorithme for pattern recognition and image interpretation. IEEE Trans. SMC-1, 24-30.

[II-1] Amemiya T., 1985. Advanced Econometrics. Basil Blackwell Ltd. : Oxford.

[II-2] Azencott R. & Dacunha-Castelle D., 1984. Séries d'Observations Irrégulières. Masson : Paris. | MR | Zbl

[II-3] Bolthausen E., 1982. On the central limit theorem for stationary mixing random fields. The Annals of Probability 10 (4), 1047-1050. | MR | Zbl | DOI

[II-4] Brillinger D.R., 1975. Times Series : Data Analysis and Theory. Holt, Rinehart and Winston : New York. | MR | Zbl

[II-5] Dacunha-Castelle D. & Duflo M., 1983. Probabilités et Statistiques, Volume 1 et 2. Masson : Paris. | MR | Zbl

[II-6] Dahlhaus R., 1983. Spectral analysis with tapered data, Journal of Time Series Analysis 4 (3), 163-175. | MR | Zbl | DOI

[II-7] Dahlhaus R., 1984. Parameter estimation of stationary processses with spectra containing strong peaks. Dans Robust and Nonlinear Time Series Analysis (eds. FRANKE, HARDLE & MARTIN), L.N.S. 26, 50-67. | MR | Zbl

[II-8] Dahlhaus R. & Künsch H., 1987. Edge effects and efficient parameter estimation for stationary random fields. Biometrika 74 (4), 877-882. | MR | Zbl | DOI

[II-9] Dzhaparidze K.O. & Yaglom A.M., 1982. Spectrum parameter estimation in time series analysis. Dans Developments in Statistics 4 (ed. KRISHNAIAH P.R.), 1-96. Academic Press : New York. | MR | Zbl

[II-10] Edwards R.E., 1979. Fourier Series. Volume 1 (second édition). Springer-Verlag : New York. | Zbl

[II-11] Guyon X., 1982. Parameter estimation for a stationary process on an d-dimensional lattice, Biometrika 69, 95-105. | MR | Zbl | DOI

[II-12] Guyon X., 1987. Estimation d'un champ par pseudo-vraisemblance conditionnelle : Etude asymptotique et application au cas markovien. Dans Spatial processes and spatial time series analysis - Proc. 6th. Franco-Belgian Meeting of Statisticians (ed. DRŒSBEKE F.). Bruxelles | MR

[II-13] Mardia K. V., 1988. Multi-dimensional multivariate gaussian Markov random fields with application to image processing. Journal of Multivariate Analysis 24, 265-284. | MR | Zbl | DOI

[II-14] Shah B.K., 1986. The distribution of positive definite quadratic forms. Dans Selected Tables in Mathematical Statistics 10 (ed. Inst. Math. Stat.). A.M.S : R.I. Providence. | MR

[II-15] Tukey J.W., 1967. An introduction to the calculations of numerical spectrum analysis, Dans Spectral Analysis of Time Series (ed. HARRIS B.), 25-46. Wiley : New York. | MR

[II-16] Walker A.M., 1964. Asyptotic properties of least-squares estimates of parameters of the spectrum of a stationary non-deterministic time serie. J. Aust. Math. Soc. 4, 363-384 | MR | Zbl | DOI

[II-17] Whittle P., 1954. On stationary processes in the plane. Biometrika 41, 434-449 | MR | Zbl | DOI