5444016
Arbitrary warped document image restoration based on segmentation and Thin-Plate Splines
Warping is a common appearance in camera captured document images. It is the primary factor that makes such kind of document images hard to be recognized. Therefore it is necessary to restore warped document image before recognition. In this paper, a novel restore method is presented. The method takes a rough line segmentation and character segmentation firstly in order to estimate the warping direction. Then several pairs of key points mapping between the original image and the restored image are determined and thin-plate splines (TPS), which is an interpolation algorithm, is introduced to restore the image. Such process can effectively describe the warping direction of the document and successfully restore the image. Some experimental results show the effect of the image restoration and compare the recognition rate before and after the restoration based on a same OCR application.
39739560
Optimal Motion Generation of Flexible Macro-micro Manipulator Systems Using Estimation of Distribution Algorithm
In this paper, a new approach for motion generation and optimization of the flexible macro-micro manipulator system is proposed based on Estimation of Distribution Algorithm (EDA). The macro-micro manipulator system is a redundant system, of which inverse kinematics remains challenging, with no generic solution to date. Here, the manipulator system configurations, or the optimal joint motions, are generated using the EDA algorithm base on Gaussian probability model. Compared with simple genetic algorithms (SGA), this approach uses fewer parameters and the time for motion optimization is remarkably reduced. The proposed approach shows excellent performance on motion generation and optimization of a flexible macro-micro manipulator system, as demonstrated by the simulation results.
18862565
Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
41660705
Fault Diagnosis on Satellite Attitude Control with Dynamic Neural Network
With increasing demands on higher performance, more safety and reliability of dynamic systems, especially on safety-critical systems, fault diagnosis became a research interests in recent years. In this paper, a systematic approach to design fault diagnosis and accommodation with compound approach such as applying improved robust observer to fault diagnosis on linearized system aided by dynamic neural network, which is trained to bridge the gap between simulated system and real system on nonlinear attributes and modeling errors. Using an instance of fault diagnosis on attitude control system of satellite attitude, advantages of new scheme are tested to be effective.
16162037
Ranking-Based Statistical Localization for Wireless Sensor Networks
This paper studies Ranking-Based localization method, which, as a special case of common Range-Based localization methods, statistically estimates node positions using the ranking-order constraints rather than the actual values of network measurements. Through extensive empirical evaluation, the advantages of Ranking-Based approach over traditional Range-Based approaches under extreme network conditions are verified. Ranking-Based approach turns out to be more robust to measurement insufficiency and more immune to measurement noise.
10034268
Stability of impulsive infinite delay differential equations
In this work, we consider the stability of impulsive infinite delay differential equations. By using Lyapunov functions and the Razumikhin technique, we get some results that are more general than ones given before. And in using the Razumikhin technique, we use a new technique that has been given by Shunian Zhang; we extend this technique to study impulsive systems. An example is also discussed in this work to illustrate the advantage of the results obtained.
481284
A Novel Method for Image Spam Filtering
As spamming morphs from text-only to multi-media in content, the existing text-based spam filters are no longer effective for the task. Image analysis technology will provide an effective tool to solve the above problem. This paper proposes a novel filter approach that focuses on the spam which change the content text to image files, or embed the spam message into attached images. Spam images are analyzed and specific features are selected for inspection based on components-based method. Then the network junk-mail-filter system can use these features to identify whether to filter the mail or not by feature matching. Experiments show that our method is efficient in inspecting document images with different geometric structure. The proposed method has potential applications in document image information extraction and filtering.
1863221
A fast and stable approach for restoration of warped document images
We present a framework for acquiring and restoring images of warped documents to their original planar shapes. Image capturing of warped documents often results in warped images. Most digital restoration approaches make use of 2D image processing methods which are dependent on the content of the images. Some recent work attempts to remove this constraint by working directly on the 3D shape of the warped documents. In our framework, we improve on this recent approach to make it more efficient and stable. First we capture the warped surface representation through a set of active lighting from a laser range scanner onto the document surface. This gives us an accurate 3D representation of the document shape. Next by using a stick constraint and a stable numerical integrator, we can digitally flatten the 3D model to a planar shape. This simple approach avoids potential instability problem and is fast. Restoration results for a few types of warped documents are demonstrated and a significant improvement over the original images can be observed.
17620967
Research on AI Planning Based Modeling Method for Area Traffic Signal Control
In this paper an area signal control model based on temporal planning is presented. Every activity of area signal control is modeled using planning domain definition language, and then the domain model of area signal control is established. Aiming at the representation problem of resource and time in the area signal control model, we extended the basic activity model by adding resource constraint and temporal constraint, and temporal planning based domain model for area signal control is established finally.
8166991
On Destination Set in IP-Level Topology Measurement
We present a thorough investigation of four issues with the destination set in IP-level topology measurement. (1) To evaluate the completeness of the destination set, we measured by brute force and found that a half of edges will be missed by the uniform random selection (URS) method which was adopted by well-known CAIDA' skitter. (2) Two potential reasons for edge missing, the short-term routing dynamics and the subnetting technique, were investigated with the last-hop selection method and the bisection selection method respectively. And the results showed that the subnetting technique is the main reason. (3) To select destinations efficiently, we presented the minimum cover selection (MCS) method which performed better than the URS method on 75% of networks over three months. (4) Finally, to investigate the bias of topology characterization, we measured a destination set larger than before and discovered a Chinese topology whose size is 5 times as large as that discovered by skitter. The comparison between characteristics of skitter's topology and ours indicated that the small destination set trends to miss the information on marginal networks.