A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Multiscale Energy-Based Model and Its Application in Some Imagery Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Model for Contours in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
A Continuous Labeling for Multiphase Graph Cut Image Partitioning
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Image Resolution Enhancement with Hierarchical Hidden Fields
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation
IEEE Transactions on Image Processing
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
| Hi-index | 0.01 |
This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws in the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at different scales, we introduce a hierarchical model involving a pyramidal label field. It combines coarse-to-fine causal interactions with a spatial neighborhood structure. This new method of segmentation, called the scale causal multigrid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation