44825167
Restoring warped document image through segmentation and full page interpolation
In camera-based optical character recognition (OCR) applications, warping is a primary problem. Warped document images should be restored before they are recognized by traditional OCR algorithm. This paper presents a novel restoration approach, which first makes an estimation of baseline and vertical direction estimation based on rough line and character segmentation, then selects several key points and determines their restoration mapping as a result of the estimation step, at last performs Thin-Plate Splines (TPS) interpolation on full page image using these key points mapping. The restored document image is expected to have straight baselines and erect character direction. This method can restore arbitrary local warping as well as keep the restoration result natural and smooth, consequently improves the performance of the OCR application. Experiments on several camera captured warped document images show effectiveness of this approach.
15661895
Robust Distributed Localization with Data Inference for Wireless Sensor Networks
We consider the range-based localization with highly insufficient inter-node distance measurements in Wireless Sensor Networks (WSNs). With an error model established, a novel distributed probabilistic localization approach which exploits the network constraints through statistical inference on missing distances is proposed. The advantage of the new approach over traditional methods is proved analytically and further confirmed by extensive simulations.
2479481
Gene Regulatory Network Construction Using Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM)
Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S. Cerevisiae cell cycle gene expression data. The experimental results demonstrate that. the accuracy of our method outperforms the previous work.
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.
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.
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.
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.
16665207
RCCtrust: A Combined Trust Model for Electronic Community
Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models: first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCCtrust – a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and community context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users' interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.
11247110
Sustaining Incentive in Grid Resource Allocation: A Reinforcement Learning Approach
Encouraging resource sharing and cooperation among different parties is one of the central goals of grid computing. In real environments, however, selfish or malicious nodes can seriously degrade the sharing and cooperation performance of a grid. To solve this problem, we propose QIA, a novel Q-learning based resource Allocation mechanism that sustains Incentive for every participating node. Exploiting an economic model, QIA recognizes the importance of trust factor when allocating resources. Each provider considers a combined metric, which is composed of the bid price and the trust value, of a requester when allocating its resources. The incomplete information is a key issue for a provider in determining the relative weight of bid price and trust value. We propose a reinforcement Q- learning technique to resolve the issue, which is able to adapt the dynamics of grid environments. We implemented QIA in a real grid test-bed, CROWN grid. Comprehensive experiments have been conducted, which demonstrate the efficacy of QIA.
8751830
Power-Modulated Challenge-Response Schemes for Verifying Location Claims
Location information should be verifiable in order to support new computing and information services. In this paper, we adapt the classical challenge-response method for authentication to the task of verifying an entity's location. Our scheme utilizes a collection of transmitters, and adapts their power allocations to verify a user's claimed location. This strategy, which we call power-modulated challenge response, is able to be used with existing wireless sensor networks, and we present three variations. First, we propose a direct method, where some transmitters are selected to send "challenges" that the claimant node should be able to witness based on its claimed location, and for which the claimant node must correctly respond in order to prove its location. Second, we reverse the strategy by presenting an indirect method, where some transmitters send challenges that the claimant node should not be able to witness. Finally, we present a signal strength based method, where the node responds with its received signal strength and thereby provides improved location verification. To evaluate our schemes, we examine different adversarial models for the claimant, and characterize the performance of our power-modulated challenge response schemes under these adversarial models.
14581660
An Improved Physically-Based Method for Geometric Restoration of Distorted Document Images
In document digitization through camera-based systems, simple imaging setups often produce geometric distortions in the resultant 2D images because of the nonplanar geometric shapes of certain documents such as thick bound books, rolled, folded, or crumpled materials, etc. Previous work [1], [2], [3], [4] has demonstrated that arbitrary warped documents can be successfully restored by flattening a 3D scan of the document. These approaches use physically-based or relaxation-based techniques in their flattening process. While this has been demonstrated to be effective in rectifying the image content and improving OCR, these previous approaches have several limitations in terms of speed and stability. In this paper, we propose a distance-based penalty metric to replace the mass- spring model and introduce additional bending resistance and drag forces to improve the efficiency of the existing approaches. The use of Verlet integration and special plane collision handling schemes also help to achieve better stability without sacrificing efficiency. Experiments on various document images captured from books, brochures, and historical documents with arbitrary warpings have demonstrated large improvements over the existing approaches in terms of stability and efficiency.