Conference Paper
Image segmentation based on Bayesian networkMarkov random field model and its application to in vivo plaque composition.
Dept. of Radiol., Washington Univ., Seattle, WA
DOI: 10.1109/ISBI.2006.1624872 Conference: Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 69 April 2006 Source: DBLP

Conference Paper: Synthetic aperture radar image segmentation based on multiscale Bayesian networks
[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we propose a multiscale Bayesian networks model and its inference algorithm. We use the multiscale Bayesian networks model to segment the Synthetic Aperture Radar (SAR) image. The multiscale Bayesian networks is constructed accordance with the multiscale sequence of SAR images, whose MAP value is performed using the Belief Propagation (BP) algorithm and the corresponding parameter estimation is finished by the ExpectationMaximization (EM) algorithm. Experimental results demonstrate that the proposed multiscale Bayesian networks model outperform the singlescale Bayesian network model and Markov Random Field  Intersecting Cortical Model (MRFICM).2013 6th International Congress on Image and Signal Processing (CISP); 12/2013  [Show abstract] [Hide abstract]
ABSTRACT: Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chainlike CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex realworld problems.IEEE Transactions on Image Processing 10/2011; · 3.11 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: We propose a new Bayesian network (BN) model for both automatic and interactive image segmentation. A multilayer BN is constructed from an oversegmentation to model the statistical dependencies among superpixel regions, edge segments, vertices, and their measurements. The BN also incorporates various local constraints to further restrain the relationships among these image entities. Given the BN model and various image measurements, belief propagation is performed to update the probability of each node. Image segmentation is generated by the most probable explanation inference of the true states of both region and edge nodes from the updated BN. Besides the automatic image segmentation, the proposed model can also be used for interactive image segmentation. While existing interactive segmentation (IS) approaches often passively depend on the user to provide exact intervention, we propose a new active input selection approach to provide suggestions for the user's intervention. Such intervention can be conveniently incorporated into the BN model to perform actively IS. We evaluate the proposed model on both the Weizmann dataset and VOC2006 cow images. The results demonstrate that the BN model can be used for automatic segmentation, and more importantly, for actively IS. The experiments also show that the IS with active input selection can improve both the overall segmentation accuracy and efficiency over the IS with passive intervention.IEEE Transactions on Image Processing 10/2011; · 3.11 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.