3331857
Table Detection via Probability Optimization
In this paper, we define the table detection problem as a probability optimization problem. We begin, as we do in our previous algorithm, finding and validating each detected table candidates. We proceed to compute a set of probability measurements for each of the table entities. The computation of the probability measurements takes into consideration tables, table text separators and table neighboring text blocks. Then, an iterative updating method is used to optimize the page segmentation probability to obtain the final result. This new algorithm shows a great improvement over our previous algorithm. The training and testing data set for the algorithm include 1, 125 document pages having 518 table entities and a total of 10, 934 cell entities. Compared with our previous work, it raised the accuracy rate to 95.67% from 90.32% and to 97.05% from 92.04%.
2061833
A machine learning based approach for table detection on the web
Table is a commonly used presentation scheme, especially for describing relational information. However, table understanding remains an open problem. In this paper, we consider the problem of table detection in web documents. Its potential applications include web mining, knowledge management, and web content summarization and delivery to narrow-bandwidth devices. We describe a machine learning based approach to classify each given table entity as either genuine or non-genuine. Various features reflecting the layout as well as content characteristics of tables are studied.In order to facilitate the training and evaluation of our table classifier, we designed a novel web document table ground truthing protocol and used it to build a large table ground truth database. The database consists of 1,393 HTML files collected from hundreds of different web sites and contains 11,477 leaf TABLE elements, out of which 1,740 are genuine tables. Experiments were conducted using the cross validation method and an F-measure of 95.89% was achieved.
5121588
Brain Surface Parameterization Using Riemann Surface Structure
We develop a general approach that uses holomorphic 1-forms to parameterize anatomical surfaces with complex (possibly branching) topology. Rather than evolve the surface geometry to a plane or sphere, we instead use the fact that all orientable surfaces are Riemann surfaces and admit conformal structures, which induce special curvilinear coordinate systems on the surfaces. Based on Riemann surface structure, we can then canonically partition the surface into patches. Each of these patches can be conformally mapped to a parallelogram. The resulting surface subdivision and the parameterizations of the components are intrinsic and stable. To illustrate the technique, we computed conformal structures for several types of anatomical surfaces in MRI scans of the brain, including the cortex, hippocampus, and lateral ventricles. We found that the resulting parameterizations were consistent across subjects, even for branching structures such as the ventricles, which are otherwise difficult to parameterize. Compared with other variational approaches based on surface inflation, our technique works on surfaces with arbitrary complexity while guaranteeing minimal distortion in the parameterization. It also offers a way to explicitly match landmark curves in anatomical surfaces such as the cortex, providing a surface-based framework to compare anatomy statistically and to generate grids on surfaces for PDE-based signal processing.
10282937
Decoding Algorithm in Statistical Machine Translation
Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heuristics used in our algorithm. We report several techniques deployed to improve the performance of the decoder. We also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. We evaluate and compare these techniques/models in our statistical machine translation system.
14266211
Efficient algorithms for minimum congestion hypergraph embedding in a cycle
The minimum congestion hypergraph embedding in a cycle (MCHEC) problem is to embed the hyperedges of a hypergraph as paths in a cycle with the same node set such that the maximum congestion (the maximum number of paths that use any single edge in the cycle) is minimized. The MCHEC problem has many applications, including optimizing communication congestions in computer networks and parallel computing. The problem is NP-hard. In this paper, we give a 1.8-approximation algorithm for the MCHEC problem. This improves the previous 2-approximation results. Our algorithm has the optimal time complexity O(mn) for a hypergraph with m hyperedges and n nodes. We also propose an algorithm which finds an embedding with the optimal congestion L* for the MCHEC problem in O(n(nL*)/sup L*/) time. This improves the previous O((mn)/sup L*+1/) time algorithm.
21757365
Estimating coronary artery lumen area with optimization-based contour detection
A modified optimization-based contour detection method was presented to compute the lumen area of the coronary artery from intravascular ultrasound (IVUS) video images. First, the search range for the artery inner wall was determined based on the continuity of IVUS video frames. Next, the internal and external energy were calculated to describe the smoothness of the arterial wall and the grayscale variation of ultrasound images, respectively. Here, a novel form of the external energy which combines the gradient and variance of the intensity of image in the radial direction was used. Finally, the minimal energy path based on the optimum contour of the artery wall was obtained using circular dynamic programming (DP). By the comparison with the typical DP procedure using the traditional external energy form, based only on the image gradient, the reliability of this modified method is considerably improved in the measurement of coronary artery lumen area.
40338116
Solving Boolean Equations Using ROSOP Forms
Boolean equations are important tools in digital logic. Previous algorithms for solving Boolean equations are based on the Boolean algebra of disjoint SOP forms. In this paper, we develop a new Boolean algebra with more efficient Boolean operation algorithms, called the reduced ordered SOP (ROSOP) forms, which are canonical representations. ROSOPs are closely related to the well-known OBDD data structure. The results here also show the algebraic structure of OBDDs.
1436962
Predicting query times
We outline the need for search engines to provide user feedback on the expected time for a query, describe a scheme for learning a model of query time by observing sample queries, and discuss the results obtained for a set of actual user queries on a document collection using the MG search engine.