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Gait recognition has become a popular research problem gaining importance for human identification based on walking style. It has emerged as an attractive research problem due to possessing several desirable merits unlike other biometrics. However, most of the existing gait recognition methods that involve Gabor-based filters suffer from the curse of dimensionality, even with the use of a dimensionality...
In this research a novel discriminative reordering model for statistical machine translation is proposed. Source dependency tree is used to define the orientation classes of the reordering model. We use maximum entropy principle to train the model. In addition to the common features used in the discriminative reordering models, two new and effective features are introduced. They are phrase number...
Detecting anomalous traffic on the Internet has remained an issue of concern for the community of security researchers over the years. Advances in computing performance, in terms of processing power and storage, have allowed the use of resource-intensive intelligent algorithms, to detect intrusive activities, in a timely manner. Naïve Bayes is a statistical inference learning algorithm with promise...
Target phrase selection, a crucial component of the state-of-the-art phrase-based statistical machine translation(PBSMT) model, plays a key role in generating accurate translation hypotheses. Inspired by context-rich word-sense disambiguation techniques, machine translation (MT) researchers have successfully integrated various types of source language context into the PBSMT model to improve target...
Modern surveillance systems often employ multiple steerable sensors that are capable of collecting information on selected objects in their environment. In this paper, we study the problem of managing these sensors adaptively to classify a collection of objects using information on their observed features. We develop a new theory for sensor management that models sensors as providing observations...
Determining the coreference of entity mentions in a discourse is a key part of the interpretation process for advanced spoken dialog applications. In this paper, we present the most comprehensive system for statistical coreference resolution in dialog to date. We also compare the impact of two contrasting theories of dialog structure (the stack model and the cache model) on the performance of statistical...
This study reviews the features used in the previous Automated Essay Scoring (AES) system, and attempts to develop a new linguistic feature-thematic feature for AES systems. According to Functional Grammar, theme is the point of departure for message, the element with which the clause is concerned. The thematic structure is an important method to promote essay coherence, and to present the message...
Segmentation is a critical and necessary procedure for computer vision, texture mapping and reverse engineering, which aims to digitally partition scanned point cloud or polygon mesh into subset which belongs to some kind of algebraic or NURBS surface. In this paper, a novel segmentation algorithm on triangulated boundary mesh is proposed based on boundary extraction and feature identification to...
A new 3D object retrieval approach is proposed based on a novel Bayesian networks lightfield descriptor (BLD). To overcome the disadvantages of the existing 3D object retrieval methods, firstly, we explore Bayesian network for building a new lightfield descriptor, 3D object is put into lightfield, and multi-views information can be obtained along a sphere, and then features of images can be extracted...
Measuring the performance of a given classifier is not a straightforward or easy task. Depending on the application, the overall classification rate may not be sufficient if one, or more, of the classes fail in prediction. This problem is also reflected in the feature selection process, especially when a wrapper method is used. Cohen's kappa coefficient is a statistical measure of inter-rater agreement...
Gesture spotting is the task of detecting and recognizing gestures defined in a vocabulary. The difficulty of gesture spotting stems from the fact that valid gestures appear sporadically in a continuous gesture stream, interspersed with invalid gestures (movements that do not correspond to any gesture contained in the vocabulary). In this paper, a novel method for designing threshold models from valid...
Integrated with the ideas of aggregation and network model, this paper presented an anomaly detection model based on DAATDM, i.e. the dynamic and aggregate anomaly detection model. Besides, it established an anomaly traffic detection system based on DAATDM. DAATDM not only analyzed the aggregation of network parameters but also built a weighted statistical model for aggregate parameters which can...
The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and...
We present a simple, fast, and effective method to detect defects on textured surfaces. Our method is unsupervised and contains no learning stage or information on the texture being inspected. The new method is based on the Phase Only Transform (PHOT) which correspond to the Discrete Fourier Transform (DFT), normalized by the magnitude. The PHOT removes any regularities, at arbitrary scales, from...
In this paper, a novel standard variance feature is proposed for background modeling in dynamic scenes involving waving trees and ripples in water. The standard variance feature is the standard variance of a set of pixels' feature values, which captures mainly co-occurrence statistics of neighboring pixels in an image patch. The background modeling method based on standard variance feature includes...
This paper presents an artificial neural network based tool that locates an ordered set of words in a text. The network model is essentially a single layer network similar to Hopfield model that uses a Hebbian approach to activate the feature layer nodes (see section 2). This model was initially developed to go with our Connectionist Associative Memory Model (CAMM) and later found to be useful in...
We propose a novel approach for satellite cloud image segmentation based on the improved Normalized Cuts Model. We extracted three important features from the multi-channel grayscale information and the texture features of satellite image, by the statistical analyses of the surface observation. Having set up the weight matrix by those features, we use the spectral graph theoretic framework of normalized...
Transcription factors play important roles in gene regulation. An accurate model that can describe the binding site of a transcription factor in the promoter region of a gene is thus the key for understanding the regulation of the gene. In this paper, we develop a new graph theoretical approach that can efficiently extract features from the binding sites of a transcription factor. These features contain...
This paper provides a novel and totally statistical method to search similar questions from a large question archive for a given queried question. Firstly, a word relevance model is trained based on the whole question archive which is made up of millions of natural language questions proposed by users on the Web. The word relevance model is utilized to find most semantically related words to a specific...
Extracting invariable features is one key issue for 3D model searching. A novel invariable feature extraction method, namely geometry projection based histogram model, is proposed for 3D model description. Different from the traditional method, one projection plane (or surface) is created for each 3D model, and the points of 3D models are projected to the projection plane (or surface), and then the...
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