Topic
Markov random field
About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.
Papers published on a yearly basis
Papers
More filters
TL;DR: Experimental results indicate that the proposed PolSAR image semantic segmentation method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.
Abstract: Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.
40 citations
TL;DR: Experimental results on real data show state-of-the-art segmentation results on this very challenging task that, if necessary, can be interactively enhanced.
Abstract: This paper presents a procedure for automatic extraction and segmentation of a class-specific object (or region) by learning class-specific boundaries. We describe and evaluate the method with a specific focus on the detection of lesion regions in uterine cervix images. The watershed segmentation map of the input image is modeled using a Markov random field (MRF) in which watershed regions correspond to binary random variables indicating whether the region is part of the lesion tissue or not. The local pairwise factors on the arcs of the watershed map indicate whether the arc is part of the object boundary. The factors are based on supervised learning of a visual word distribution. The final lesion region segmentation is obtained using a loopy belief propagation applied to the watershed arc-level MRF. Experimental results on real data show state-of-the-art segmentation results on this very challenging task that, if necessary, can be interactively enhanced.
40 citations
TL;DR: The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results and the sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone.
40 citations
TL;DR: This paper examines the connection between loss networks without controls and Markov random field theory and yields insight into the structure and computation of network equilibrium distributions, and into the nature of spatial dependence in networks.
Abstract: This paper examines the connection between loss networks without controls and Markov random field theory. The approach taken yields insight into the structure and computation of network equilibrium distributions, and into the nature of spatial dependence in networks. In addition, it provides further insight into some commonly used approximations, enables the development of more refined approximations, and permits the derivation of some asymptotically exact results.
40 citations
30 Oct 1994
TL;DR: An algorithm is described for segmenting 3D MR brain image into K different tissue types, which include gray, white matter and CSF, and maybe other abnormal tissues, which has the potential to routinely process clinical MR images with minimal user involvement.
Abstract: An algorithm is described for segmenting 3D MR brain image into K different tissue types, which include gray, white matter and CSF, and maybe other abnormal tissues. MR images considered can be either scale- or multi-valued. Each scale-valued image is modeled as a collection of regions with slowly varying intensity plus a white Gaussian noise. Each tissue type is modeled by a Markov random field with the second order neighborhood in a 3D lattice. The proposed algorithm is an adaptive K-means clustering algorithm for 3-dimensional and multi-valued images. Each iteration consists of two steps: estimate mean intensity at each location for each type, and estimate tissue types by maximizing the a posteriori probability. The algorithm slowly adapts to the local intensity variation of each region, so it is robust to the "shading" effect. It has the potential to routinely process clinical MR images with minimal user involvement. Its performance is tested using patient data. >
40 citations