Abstract
This work deals with unsupervised sonar image segmentation. We present a new estimation segmentation procedure using the recent iterative method of estimation called Iterative Conditional Estimation (ICE) [1]. This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label field (modeled by a Markov Random Field (MRF)). For the estimation step, we use a maximum likelihood technique to estimate the noise model parameters, and the least squares method proposed by Derin et al. [2] to estimate the MRF prior model. Then, in order to obtain an accurate segmentation map and to speed up the convergence rate, we use a multigrid strategy exploiting the previously estimated parameters. This technique has been successfully applied to real sonar images 1, and is compatible with an automatic processing of massive amounts of data.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
F. Salzenstein and W.Pieczinsky. Unsupervised bayesian segmentation using hidden markovian fields. In proc. ICASSP'95, pages 2411–2414, May 1995.
H. Denn and H. Elliot. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. Pattern Anal. and Machine Intell., PAMI-9(1):39–55, January 1987.
F. Schmitt, M. Mignotte, C. Collet, and P. Thourel, Estimation of noise parameters on sonar images. In Signal and Image Processing, SPIE'96, volume 2823, pages 1–12, Denver, Colorado, USA, August 1996.
J. Besag. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, B-48:259–302, 1986.
C. Collet, P. Thourel, P. Pérez, and P. Bouthemy. Hierarchical MRF modeling for sonar picture segmentation. In proc. ICIP'96, Lausanne, September 1996.
S. Lakshmanan and H. Derin. Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. IEEE Trans. on PAMI, 11(8):799–813, August 1989.
J.G. Postaire and P.A. Vasseur. An approximate solution to normal mixture identification with application to unsupervised pattern classification. IEEE Trans. on PAMI, (2):163–179, 1981.
G. Celleux and J. Diebolt. A random imputation principle: the stochastic EM algorithm. Technical Report 901, INRIA, September 1988.
A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Royal Statistical Society, pages 1–38, 1976.
Z. Kato, J. Zerubia, and M. Berthod. Unsupervised parallel image classification using a hierarchical Markovian model. In Proc. ICCV'95, volume 1, pages 169–174, Cambridge, Massachusetts, 20–23 June 1995.
B. Chalmond. An iterative gibbsian technique for reconstruction of m-ary images. Pattern Recognition, 22(6):747–761, 1989.
M. Mignotte. Modèle markovien: estimation des paramètres. Technical Report 7, Ecole Navale, March 1996.
Y. Kim and H.S Yang. Efficient image labelling based on Markov random field and error backpropagation network. Pattern Recognition, 26(11):1695–1707, 1993.
P. A. Kelly, H. Derin, and K. D. Hart. Adaptative segmentation of speckled images using a hierarchical random field model. IEEE Transactions on Acoustic Speech and Signal Processing, 36(10): 1628–1641, October 1988.
S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. and Machine Intell., PAMI-6(6):721–741, November 1984.
J. W. Goodman. Some fundamental properties of speckle. Journal of Optical Society of America, 66(11):1145–1150, November 1976.
L. Younes. Parametric inference for imperfectly observed Gibbsian fields. Springer-Verlag Prob. Th. Rel. Fields 82, pages 625–645, 1989.
S. Banks. Signal processing image processing and pattern recognition, 1990.
B. Braathen, P. Masson, and W. Pieczynski. Global and local methods of unsupervised Bayesian segmentation of images. GRAPHICS and VISION, (1):39–52, 1993.
F. Heitz, P. Pérez, and P. Bouthemy. Multiscale minimisation of global energy functions in some visual recovery problems. In Computer Vision Graph. and Image Proces.: Image Understanding, volume 59, January 1994.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mignotte, M., Collet, C., Pérez, P., Bouthemy, P. (1997). Unsupervised segmentation applied on sonar images. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_99
Download citation
DOI: https://doi.org/10.1007/3-540-62909-2_99
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62909-2
Online ISBN: 978-3-540-69042-9
eBook Packages: Springer Book Archive