• Present modeling abstraction that we call random field optimization. Abstraction captures uncertainty lives on continuous space-time domains. Demonstrate the applicability using case studies. We present a new paradigm for optimization Random fields are powerful aims to capture behavior of variables live infinite-dimensional spaces (e.g., space and time) such as stochastic processes time serie...

In this paper, we present a new background estimation algorithm which effectively represents both background and foreground. The problem is formulated with a labeling problem over a patch-based Markov random field (MRF) and solved with a graph-cuts algorithm. Our method is applied to the problem of mosaic blending considering the moving objects and exposure variations of rotating and zooming ca...

We address the problem of structure mapping that arises in xml data exchange or xml document transformation. Our approach relies on xml annotation with semantic labels that describe local tree editions. We propose xml Conditional Random Fields (xcrfs), a framework for building conditional models for labeling xml documents. We equip xcrfs with efficient algorithms for inference and parameter est...

Left atrium segmentation and the extraction of its geometry remains a challenging problem despite of existing approaches. It is a clinically-relevant important problem with an increasing interest as more research into the mechanism of atrial fibrillation and its recurrence process is undertaken. Contrast-Enhanced (CE) Magnetic Resonance Angiography (MRA) produces excellent images for extracting...

Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes. The existing graph estimation methods built on Gaussian graphical models and covariance selection algorithms can not handle such data, neither can the theories developed around such methods be directly appli...

The potential benefits of mining social media to learn about adverse drug reactions (ADRs) are rapidly increasing with the increasing popularity of social media. Unknown ADRs have traditionally been discovered by expensive post-marketing trials, but recent work has suggested that some unknown ADRs may be discovered by analyzing social media. We propose three methods for extracting ADRs from for...

Recently, categorical grammar has been focused as a powerful grammar. This paper aims to develop a framework for automatic CG tagging for Thai. We investigated two main algorithms, CRF and Statistical alignment model based on information theory (SAM). We found that SAM gives the best results both in word level and sentence level. We got the accuracy 89.25% in word level and 82.49% in sentence l...

Large image-based rendering data sets such as light fields require efficient compression and random access to individual images for applications such as interactive streaming to a remote user. In our earlier work, we propose a theoretical framework to analyze the trade-off between compression efficiency and random access. In this current paper, we extend the theoretical framework by calculating...