Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Kalman filtering in pairwise Markov trees
Signal Processing
Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
SAR image segmentation based on mixture context and wavelet hidden-class-label Markov random field
Computers & Mathematics with Applications
Boundary refinements for wavelet-domain multiscale texture segmentation
Image and Vision Computing
GPU-accelerated MRF segmentation algorithm for SAR images
Computers & Geosciences
Incorporating shape into spatially-aware adaptive object segmentation algorithm
Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
Markov random fields for improving 3D mesh analysis and segmentation
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Multiscale discriminant saliency for visual attention
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Edge multi-scale markov random field model based medical image segmentation in wavelet domain
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data