|
Poggio T,et al. Computational vision and regularization theory[J].Nature.1985, 317:314-319[2]Marroquin J,et al. Probabilistic solution of ill-posed problems in computational vision[J].J. Am.Statist. Assoc.1987, 82(397):76-89[3]Bertero M,et al. Ill-posed problems in early vision[J].Proc.IEEE.1988, 76(8):869-889[4]Gamble E, et al. Integration of vision modules and labeling of surface discontinuities. IEEE Trans. on Syst. Man Cybern., 1989, SMC-19(6): 1576-1581.[5]Chou K,et al. Multiscale recursive estimation, data fusion, and regularization. IEEE Trans. on Automatic Control, 1994, AC-39(3): 464-478.[6]Daniel M, Willsky A. A multiresolution methodology for signal-level fusion and data assimilation with applications to remote sensing[J].Proc. IEEE.1997, 85(1):164-180[7]Besag J. Spatial interactions and the statistical analysis of lattice systems(with discussion), J. Roy. Statist. Soc., 1974, B36(2): 192-236.[8]Frank O, Strauss D. Markov graphs[J].J. Amer. Statist. Assoc.1986, 81(395):832-842[9]Lakshmanan S, Derin H. Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annaling. IEEE Trans. on Pattern Anal. Machine Intell., 1989, PAMI-11(8): 799-813.[10]Jeng F, Woods J. Compound Gauss Markov random fields for image estimation. IEEE Trans. on Signal Processing, 1991, SP-39(3): 683-691.[11]Jeng F, Woods J. Simulated annealing in compound Gaussian random fields. IEEE Trans. on Inform. Theory, 1990, IT-36(1): 94-107.[12]Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images[J].IEEE Trans. on Pattern Anal. Machine Intell.1984, PAMI-6(6):721-741[13]Simchony T,et al. Pyramid implementation of optimal-step conjugate-search algorithms for some low-level vision problems. IEEE Trans. on Syst. Man Cybern., 1989, SMC-19(6): 1408-1424.[14]Fwu J, Djuric P. Unsupervised vector image segmentation by a tree structure-ICM algorithm. IEEE Trans. on Medical Imaging, 1996, MI-15(6): 871-881.[15]Bouman C, Liu B. Multiple resolution segmentation of textured images. IEEE Trans. on Pattern Anal. Machine Intell., 1991, PAMI-13(2): 99-113.[16]Hiriyannaiah H,et al. Restoration of piecewise-constant images by mean-field annealing. J. Opt.Soc. Am., 1989, A-6(12): 1901-1912.[17]Zerubia J, Chellappa R. Mean field annealing using compound Gauss-Markov random fields for edge detection and image estimation. IEEE Trans. on Neural Networks, 1993, NN-4(4): 703-709.[18]Yuille A. Energy functions for early vision and analog networks. Biol. Cybern., 1989, 61(2):115-123.[19]Derin H, Elliott H. Modeling and segmentation of noisy and textured images using Gibbs random fields[J].IEEE Trans. on Pattern Anal. Machine Intell.1987, PAMI-9(1):39-55[20]Barzohar M, Cooper D. Automatic finding of main roads in aerial images by using geometricstochastic models and estimation. IEEE Trans. on Pattern Anal. Machine Intell., 1996, PAMI-18(7): 707-721.[21]Gunsel B,et al. Reconstruction and boundary detection of range and intensity images using multiscale MRF representations[J].Computer Vision and Image Understanding.1996, 63(2):353-366[22]Moura J, Balram N. Recursive structure of noncausal Gauss-Markov random fields. IEEE Trans.on Inform. Theory, 1992, IT-38(2): 334-354.[23]Zhang J. The mean field theory in EM procedures for blind Markov random field image restoration. IEEE Trans. on Image Processing, 1993, IP-2(1): 27-40.[24]Geiger D, Girosi F. Parallel and deterministic algorithms from MRFs: Surface reconstruction. IEEE Trans. on Pattern Anal. Machine Intell., 1991, PAMI-13(5): 401-412.[25]Zhang J,et al. The application of mean field theory to image motion estimation. IEEE Trans. on Image Processing, 1995, IP-4(1): 19-33.[26]Zhang J. The application of the Gibbs-Bogoliubov-Feynman inequality in mean field calculations for Markov random fields. IEEE Trans. on Image Processing, 1996, IP-5(7): 1208-1214.[27]陆明俊 ,王润生．一种基于多尺度MRF模型的边缘检测算法．电子学报，1999, 27(8)：82-86.[28]Besag J. On the statistical analysis of dirty pictures.J. Roy. Statist.Soc.,1986, B-48(3):259-302.[29]Figueiredo M, Leitao J. Unsupervised image restoration and edge location using compound Gauss-Markov random fields and the MDL principle. IEEE Trans. on Image Processing, 1997, IP-6(8):1089-1102.[30]Silverman J, Cooper D. Bayesian clustering for unsupervised estimation of surface and texture models. IEEE Trans. on Pattern Anal. Machine Intell., 1988, PAMI-10(4): 482-495.
|
下载:
