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.
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07 Dec 2012
TL;DR: Evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling.
Abstract: Based on Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF), we propose a novel background modeling method for motion detection in dynamic scenes. The key idea of the proposed approach is the successful introduction of the spatial-temporal constraints into the T2-FGMM by a Bayesian framework. The evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling.
78 citations
TL;DR: In this paper, an effective stochastic geological modeling framework is proposed based on Markov random field theory, which is conditional on site investigation data, such as observations of soil types from ground surface, borehole logs, and strata orientation from geophysical tests.
78 citations
01 Aug 2012
TL;DR: A new technique for incorporation of local information for unsupervised change detection in multitemporal remote sensing images is introduced, which is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.
Abstract: In this paper we have used two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) along with local information for unsupervised change detection in multitemporal remote sensing images. In conventional FCM and GKC no spatio-contextual information is taken into account and thus the result is not so much robust to small changes. Since the pixels are highly correlated with their neighbors in image space (spatial domain), incorporation of local information enhances the performance of the algorithms. In this work we have introduced a new technique for incorporation of local information. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Two fuzzy cluster validity measures (Xie-Beni and fuzzy hypervolume) have been used to quantitatively evaluate the performance. Results are compared with those of existing state of the art Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.
78 citations
TL;DR: Multicoil2 is presented, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences and significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coil sequences.
Abstract: The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu.
77 citations
TL;DR: The fundamental principle of equivalence of ensembles provides deep insights into questions such as the origin of MRF models, typical images of statistical models, and error rates in various texture related vision tasks and the asymmetry phenomenon observed in texture “pop-out” experiments is explained.
Abstract: In the past thirty years, research on textures has been pursued along two different lines. The first line of research, pioneered by Julesz (1962, IRE Transactions of Information Theory, IT-8:84–92), seeks essential ingredients in terms of features and statistics in human texture perception. This leads us to a mathematical definition of textures in terms of Julesz ensembles (Zhu et al., IEEE Trans. on PAMI, Vol. 22, No. 6, 2000). A Julesz ensemble is a set of images that share the same value of some basic feature statistics. Images in the Julesz ensemble are defined on a large image lattice (a mathematical idealization being Z2) so that exact constraint on feature statistics makes sense. The second line of research studies Markov random field (MRF) models that characterize texture patterns on finite (or small) image lattice in a statistical way. This leads us to a general class of MRF models called FRAME (Filter, Random field, And Maximum Entropy) (Zhu et al., Neural Computation, 9:1627–1660). In this article, we bridge the two lines of research by the fundamental principle of equivalence of ensembles in statistical mechanics (Gibbs, 1902, Elementary Principles of Statistical Mechanics. Yale University Press). We show that 1). As the size of the image lattice goes to infinity, a FRAME model concentrates its probability mass uniformly on a corresponding Julesz ensemble. Therefore, the Julesz ensemble characterizes the global statistical property of the FRAME models 2). For a large image randomly sampled from a Julesz ensemble, any local patch of the image given its environment follows the conditional distribution specified by a corresponding FRAME model. Therefore, the FRAME model describes the local statistical property of the Julesz ensemble, and is an inevitable texture model on finite (or small) lattice if texture perception is decided by feature statistics. The key to derive these results is the large deviation estimate of the volume of (or the number of images in) the Julesz ensemble, which we call the entropy function. Studying the equivalence of ensembles provides deep insights into questions such as the origin of MRF models, typical images of statistical models, and error rates in various texture related vision tasks (Yuille and Coughlan, IEEE Trans. on PAMI, Vol. 2, No. 2, 2000). The second thrust of this paper is to study texture distance based on the texture models of both small and large lattice systems. We attempt to explain the asymmetry phenomenon observed in texture “pop-out” experiments by the asymmetry of Kullback-Leibler divergence. Our results generalize the traditional signal detection theory (Green and Swets, 1988, Signal Detection Theory and Psychophysics, Peninsula Publishing) for distance measures from iid cases to random fields. Our theories are verified by two groups of computer simulation experiments.
77 citations