18726671
Adaptive image-smoothing using a coplanar matrix and its application to document image binarization
Abstract. For document images corrupted by various kinds of noise, direct binarization images may be severely blurred and degraded. A common treatment for this problem is to pre-smooth input images using noise-suppressing filters. This article proposes an image-smoothing method used for prefiltering the document image binarization. Conceptually, we propose that the influence range of each pixel affecting its neighbors should depend on local image statistics. Technically, we suggest using coplanar matrices to capture the structural and textural distribution of similar pixels at each site. This property adapts the smoothing process to the contrast, orientation, and spatial size of local image structures. Experimental results demonstrate the effectiveness of the proposed method, which compares favorably with existing methods in reducing noise and preserving image features. In addition, due to the adaptive nature of the similar pixel definition, the proposed filter output is more robust regarding different noise levels than existing methods.
15710341
Motion deblurring for optical character recognition
This paper investigates the problem of blurring caused by motion during image capture of text documents. Motion blurring prevents proper optical character recognition of the document text contents. One area of such applications is to deblur name card images obtained from handheld cameras. In this paper, a complete motion deblurring procedure for document images has been proposed. The method handles both uniform linear motion blur and uniform acceleration motion blur. Experiments on synthetic and real-life blurred images prove the feasibility and reliability of this algorithm provided that the motion is not too irregular. The restoration procedure consumes only small amount of computation time.
206742243
Information Retrieval in Document Image Databases
With the rising popularity and importance of document images as an information source, information retrieval in document image databases has become a growing and challenging problem. In this paper, we propose an approach with the capability of matching partial word images to address two issues in document image retrieval: word spotting and similarity measurement between documents. First, each word image is represented by a primitive string. Then, an inexact string matching technique is utilized to measure the similarity between the two primitive strings generated from two word images. Based on the similarity, we can estimate how a word image is relevant to the other and, thereby, decide whether one is a portion of the other. To deal with various character fonts, we use a primitive string which is tolerant to serif and font differences to represent a word image. Using this technique of inexact string matching, our method is able to successfully handle the problem of heavily touching characters. Experimental results on a variety of document image databases confirm the feasibility, validity, and efficiency of our proposed approach in document image retrieval.
38236882
Chart analysis and recognition in document images
Hidden Markov models are a probabilistic modeling tool for time series data. It has been successfully applied to many areas, such as speech recognition, hand-written character recognition, etc. In this paper, we present a novel statistical approach using ergodic hidden Markov models to recognize scientific charts. We also present a newly developed feature extraction method for chart images. Unlike traditional primitive-based diagram recognition method, our approach need not recognize the graphic primitives in charts thus bypassing the recognition error problem caused by the inaccurate primitive extraction that is also a major obstacle to the construction of a general chart recognition system.
5976969
Unsupervised Relation Disambiguation Using Spectral Clustering
This paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. It works by calculating eigen-vectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. Experiment results on ACE corpora show that this spectral clustering based approach outperforms the other clustering methods.
2536406
Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
In this paper we focus on how to improve pronoun resolution using the statistics-based semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such information: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. Our study shows that the semantic compatibility obtained from the web can be effectively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns.
16019676
A Distributed Throttling Approach for Handling High Bandwidth Aggregates
Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding-based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a family of algorithms that "proactively" protect a server from overload by installing rate throttles in a set of upstream routers. Based on an optimal control setting, we propose algorithms that achieve throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms 1) are highly adaptive by avoiding unnecessary parameter configuration, 2) provide max-min fairness for any number of throttling routers, 3) respond very quickly to network changes, 4) are extremely robust against extrinsic factors beyond the system control, and 5) are stable under given delay bounds.
11363576
Functional Principles of Registry-based Service Discovery
As service discovery protocols (SDP) are becoming increasingly important for ubiquitous computing, they must behave according to predefined principles. We present the functional principles of service discovery for robust, registry-based service discovery. A methodology to guarantee adherence to these principles is provided and illustrated by formal verification of the principles against FRODO, an SDP built for the home environment. We show that, to make behavioral guarantees, an SDP has to be robust against network disturbances, and cannot rely only on the network layer
18402501
An Empirical Investigation of the Auction Buyer's Choice to Buy Out or Bid: Cry of Regret or Laugh of Satisfaction?
Online auction buyers often face the predicament of choosing between the buyout strategy and the bidding strategy to acquire an auctioned product. A buyer who chooses to buy out will obtain the item immediately at the posted price while the one who chooses to bid will have to monitor the bidding process in the hope of acquiring the product at a price lower than the posted one. Despite the wealth of auction literature, relatively few studies empirically assess the buyout option. This study seeks to bridge the gap by investigating how the buyout option (permanent or temporary) and price parameters (starting price and buyout price) affect the formation of two emotions, namely anticipated satisfaction of choice and anticipated regret of outcome, which in turn affect the buying strategy (i.e., buy out or bid). We employ laboratory experiments in our study and our results indicate two interesting findings. First, when the permanent buyout option is available, a buyer is more likely to exhibit loss aversion characteristics and prefer to adopt the buyout strategy. Second, when the temporary buyout option is present, the thought that one could avoid premature ending of auction by exercising the bidding option at the start decreases one’s preference for adopting the buyout strategy. Implications of these results for further research and practice are discussed.
32509548
Chosen Ciphertext Security from Identity-Based Encryption Without Strong Condition
Recently, Canetti et al [11] gave a generic construction (called CHK construction) of public key encryption (PKE) from a selective identity-based encryption scheme combined with a strong one-time signature scheme. Later, few schemes were proposed to improve the efficiency of CHK construction [11], for example, Boneh-Katz scheme [8] replaced a strong one-time signature with a message authentication code and Boyen-Mei-Waters scheme [9] was constructed directly from Waters' IBE scheme. But, both constructions have either trade-off the publicly verifiable property or security against adaptive chosen-ciphertext attack. We ask a question whether it is possible to construct an efficient and publicly verifiable PKE scheme from a selective IBE scheme with a weak one-time signature scheme. In this paper, we provide an affirmative answer and construct a public key encryption scheme which preserves the publicly verifiable property and is secure against adaptive chosen-ciphertext attack. The construction of the proposed scheme is based on Boneh-Boyen identity-based encryption (IBE) scheme [5] and a weak one-time signature scheme (using Waters' signature scheme [24]) built within Boneh-Boyen IBE scheme. In this construction, one-time signature scheme is not required to be strongly existential unforgeable as Waters' signature scheme is not a strongly existential unforgeability. We also show that the proposed scheme is ”almost” as efficient as the original Boneh-Boyen IBE scheme.
5965390
The use of bigrams to enhance text categorization
In this paper, we present an efficient text categorization algorithm that generates bigrams selectively by looking for ones that have an especially good chance of being useful. The algorithm uses the information gain metric, combined with various frequency thresholds. The bigrams, along with unigrams, are then given as features to two different classifiers: Naive Bayes and maximum entropy. The experimental results suggest that the bigrams can substantially raise the quality of feature sets, showing increases in the break-even points and F1 measures. The McNemar test shows that in most categories the increases are very significant. Upon close examination of the algorithm, we concluded that the algorithm is most successful in correctly classifying more positive documents, but may cause more negative documents to be classified incorrectly.