Robust regression methods for computer vision: a review
International Journal of Computer Vision
Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
Signal Processing, Image Processing and Pattern Recognition
Signal Processing, Image Processing and Pattern Recognition
The Asymmetric Generalized Gaussian Function: A New HOS-Based Model for Generic Noise Pdfs
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
An equivalence of the EM and ICE algorithm for exponential family
IEEE Transactions on Signal Processing
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
EURASIP Journal on Applied Signal Processing
Fuzzy pairwise Markov chain to segment correlated noisy data
Signal Processing
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
Learning conditional random fields for classification of hyperspectral images
IEEE Transactions on Image Processing
Journal of Signal Processing Systems
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This paper presents an unsupervised method to segment multispectral images, involving a correlated non-Gaussian noise. The efficiency of the Markovian quadtree-based method we propose will be illustrated on a satellite image segmentation task with multispectral observations, in order to update nautical charts. The proposed method relies on a hierarchical Markovian modeling and includes the estimation of all involved parameters. The parameters of the prior model are automatically calibrated while the estimation of the noise parameters is solved by identifying generalized distribution mixtures [P. Rostaing, J.-N. Provost, C. Collet, Proc. International Workshop EMMCVPR'99: Energy Minimisation Methods in Computer Vision and Pattern Recognition, Springer Verlag, New York, 1999, p. 141], by means of an iterative conditional estimation (ICE) procedure. Generalized Gaussian (GG) distributions are considered to model various intensity distributions of the multispectral images. They are indeed well suited to a large variety of correlated multispectral data. Our segmentation method is applied to Satellite Pour l'Observation de la Terre (SPOT) remote multispectral images. Within each segmented region, a bathymetric inversion model is then estimated to recover the water depth map. Experiments on different real images have demonstrated the efficiency of the whole process and the accuracy of the obtained results has been assessed using ground truth data. The designed segmentation method can be extended to images for which it is required to segment a region of interest using an unsupervised approach.