We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accurac...

The soil is a heterogeneous and anisotropic medium. Hydraulic conductivity, an intrinsic property of natural alluvial deposits varies both deterministically and randomly in space and has different values in various directions. In the present study, the permeability of natural deposits and its influence on the seepage flow through a natural alluvial deposit is studied. The 2D Finite Difference c...

Most information extraction (IE) systems treat separate potential extractions as independent. However, in many cases, considering influences between different potential extractions could improve overall accuracy. Statistical methods based on undirected graphical models, such as conditional random fields (CRFs), have been shown to be an effective approach to learning accurate IE systems. We pres...

Word clustering is a conventional and important NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distributional similarity and the other relies on the co-occurrence of two words in lexicosyntactic patterns. Although the two methods have been discussed separately, it is promising to combine them since they are complementary with each other. ...

This paper presents a novel tissue proliferation model of extraneuronal filaceous material consisting of accumulation of the amyloid beta-proteins. Proposed model is constructed using a 3-D Gaussian Hidden Markov Random Field obtained from fluorescent microscopy measurements.

In this article we propose a modification to the HMRF-EM framework applied to image segmentation. To do so, we introduce a new model for the neighborhood energy function of the Hidden Markov Random Fields model based on the Hidden Markov Model formalism. With this new energy model, we aim at (1) avoiding the use of a key parameter chosen empirically on which the results of the current models ar...

In this project1, we study the hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named HMRF-EM-image for 2D image segmentation using the HMRF-EM framework2. This toolbox also implements edge-prior-preserving image segmentation, and can be easily reconfigured for other problems, such as 3D image segmentation.

The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover changes at a high frequency. A simultaneous classification of images from different epochs that is also capable of detecting changes is achieved...