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In this paper we propose a novel personalized paper search system using the relevance among user's queried keywords and user's behaviors on a searched paper list. The proposed system builds user's individual relevance network from analyzing the appearance frequencies of keywords in the searched papers. The relevance network is personalized by providing weights to the appearance frequencies of keywords according to users' behaviors on the searched list, such as "downloading," "opening," and "no-action." In the experimental section, we demonstrate our method using 100 faculties' search information in the University of Suwon. [ABSTRACT FROM AUTHOR]

In this paper we present a method for optical paper surface roughness measurement, which overcomes the disadvantages of the traditional methods. Airflow-based roughness measurement methods and profilometer require expensive special equipment, essential laboratory conditions, are contact-based and slow and unsuitable for on-line control purposes methods. We employed an optical microscope with a built-in CCD-camera to take images of paper surface. The obtained image is considered as a texture. We applied statistical brightness measures and fractal dimension analysis for texture analysis. We have found a strong correlation between the roughness and a fractal dimension. Our method is non-contact-based, fast and is suitable for on-line control measurements in the paper industry. [ABSTRACT FROM AUTHOR]

The techniques for organizing and retrieving the artifacts from digital libraries (DLs) semantically are discussed, which include letting the taxonomies and semantic relations work in tandem to index the artifacts in DLs; integrating the techniques used in natural language processing and taxonomies to help users to start their retrieval processes; and ranking scientific papers on similarity in terms of contents or ranking the relevant papers on multi-factors. These techniques are verified through the design and implementation of a prototype of DLs for scientific paper management. [ABSTRACT FROM AUTHOR]

A novel method that is able to simulate artistic effects of ink-refusal and stroke-trace-reservation in ink paintings is developed. The main ingredients of ink are water, carbon particles, as well as glue. However, glue is not taken into account in other researches, although it plays an important role in ink diffusion. In our ink-diffusion model, we consider the number of fibers and the quantity of glue as parameters of the structure of paper. We simulate the physical interaction among water, carbon particles, glue, and fiber mesh of paper. The realistic renderings created from our models have demonstrated that our models are successful, and are able to imitate the special artistic effects of ink painting. [ABSTRACT FROM AUTHOR]

Increasing the smooth degree of data series is key factor of grey model's precision. In this paper, the more general method is put forward on the basis of summarizing several kinds of ways to improve smooth degree of data series, and a new transformation is represented. The practical application shows the effectiveness and superiority of this method. [ABSTRACT FROM AUTHOR]

This paper discusses two statistical shape descriptors, the Edge Co-occurrence Matrix (ECM) and the Contour Co-occurrence Matrix (CCM), and their use in surface defect classification. Experiments are run on two image databases, one containing metal surface defects and the other paper surface defects. The extraction of Haralick features from the matrices is considered. The descriptors are compared to other shape descriptors from e.g. the MPEG-7 standard. The results show that the ECM and the CCM give superior classification accuracies. [ABSTRACT FROM AUTHOR]

This paper addresses a capacitated production planning (CPP) problem with outsourcing. Specially, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production, inventory, outsourcing, and sale loss without postponement or backlog; (2) The goal of the solution to this problem is to minimize the total cost covering the production, setup, inventory holding, sale loss, and outsourcing activities; (3) different from previous studies which usually involved some special assumptions on the cost function for these activities, the cost function structure we consider in this paper are arbitrary and time-varying; and (4) we consider a general problem situation where the production, inventory, and outsourcing have a capacity limitation. It is likely that there is no solution in the model due to the consideration of such general cost functions and many constraints, hence we employ a Bellman/Ford algorithm for checking feasible solutions of the problem and resulting in a feasible solution region. After that, a proposed algorithm is applied for searching a global optimal solution with in the feasible solution region. [ABSTRACT FROM AUTHOR]

This paper investigates the fault detection and prediction of rhythmically soniferous products, such as clocks, watches and timers. Such products with fault cannot work steadily or probably cause malfunction. The authors extend the concept of computer audition and establish an architectural model of product fault prediction system based on probabilistic neural networks. The system listens to the product sound by the multimedia technology and the sound features are extracted to detect and predict faults by the neural network. The paper analyzes the reasons of timer faults and the corresponding sound features. Experiments are made in the laboratory to demonstrate the proposed method. The technology is expected to apply in factories in coming years for realizing automatic product test and improving efficiency of product inspection. [ABSTRACT FROM AUTHOR]

In this paper we tackle the specific problem of old documents recovery. Spots, print through, underlines and others ageing features are undesirable not only because they harm the visual appearance of the document, but also because they affect future Optical Character Recognition (OCR). This paper proposes a new method integrating fuzzy clustering of color properties of original images and mathematical morphology. We will show that this technique leads to higher quality of the recovered images and, at the same time, it delivers cleaned binary text for OCR applications. The proposed method was applied to books of XIX Century, which were cleaned in a very effective way. [ABSTRACT FROM AUTHOR]

Changes in a speaker's emotion are a fundamental component in human communication. Some emotions motivate human actions while others add deeper meaning and richness to human interactions. In this paper, we explore the development of a computing algorithm that uses audio and visual sensors to recognize a speaker's affective state. Within the framework of Multi-stream Hidden Markov Model (MHMM), we analyze audio and visual observations to detect 11 cognitive/emotive states. We investigate the use of individual modality confidence measures as a means of estimating weights when combining likelihoods in the audio-visual decision fusion. Person-independent experimental results from 20 subjects in 660 sequences suggest that the use of stream exponents estimated on training data results in classification accuracy improvement of audio-visual affect recognition. [ABSTRACT FROM AUTHOR]

We consider the problem of recognizing face or object when only single training image per class is available, which is typically encountered in law enforcement, passport or identification card verification, etc. In such cases, many discriminant subspace methods such as Linear Discriminant Analysis (LDA) fail because of the non-existence of intra-class variation. In this paper, we propose a novel framework called 2-Dimensional Kernel PCA (2D-KPCA) for face or object recognition from a single image. In contrast to conventional KPCA, 2D-KPCA is based on 2D image matrices and hence can effectively utilize the intrinsic spatial structure information of the images. On the other hand, in contrast to 2D-PCA, 2D-KPCA is capable of capturing part of the higher-order statistics information. Moreover, this paper reveals that the current 2D-PCA algorithm and its many variants consider only the row information or column information, which has not fully exploited the information contained in the image matrices. So, besides proposing the unilateral 2D-KPCA, this paper also proposes the bilateral 2D-KPCA which could exploit more information concealed in the image matrices Furthermore, some approximation techniques are developed for improving the computational efficiency. Experimental results on the FERET face database and the COIL-20 object database show that: 1) the performance of KPCA is not necessarily better than that of PCA; 2) 2D-KPCA almost always outperforms 2D-PCA significantly; 3) the kernel methods are more appropriate on 2D pattern than on 1D patterns. [ABSTRACT FROM AUTHOR]

This paper summarizes a new concept to determine principal curves for nonlinear principal component analysis (PCA). The concept is explained within the framework of the Hastie and Stuetzle algorithm and utilizes spline functions. The paper proposes a new algorithm and shows that it provides an efficient method to extract underlying information from measured data. The new method is geometrically simple and computationally expedient, as the number of unknown parameters increases linearly with the analyzed variable set. The utility of the algorithm is exemplified in two examples. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a fast affine transform method for real-time machine vision applications. Inspection of parts by machine vision requires accurate, fast, reliable, and consistent operations, where the transform of visual images plays an important role. Image transform is generally expensive in computation for real-time applications. For example, a transform including rotation and scaling would require four multiplications and four additions per pixel, which is going to be a great burden to process a large image. Our proposed method reduces the complexity substantially by removing four multiplications per pixel, which exploits the relationship between two neighboring pixels. In addition, this paper shows that the affine transform can be performed by fixed point operations with marginal error. Two interpolation methods are also tried on top of the proposed method in order to test the feasibility of fixed point operations. Experimental results indicated that the proposed algorithm was about six times faster than conventional ones without any interpolation and five times faster with bilinear interpolation. [ABSTRACT FROM AUTHOR]

The Although a variety of standardization and Learning Management System (LMS) were produced to develop and effectively manage web contents in response to active diffusion of internet application, practical changes to assist online learners are not yet to be found. The core purpose of the LMS system proposed in this paper is to help learners pursue proactive and self-oriented education by allowing learners to proactively configure their own content, that is, learners no longer have to be restricted by prescribed sequence of lectures. In this paper, I would like to introduce a LMS that can support self-leading education by providing various types of learners at Virtual University with delicately organized educational contents for maximum efficiency. The system allows a learner to select a lecture or a chapter which has been presorted to meet his educational needs and intellectual ability. [ABSTRACT FROM AUTHOR]

The face images are varying environment database from external illumination. Therefore we propose parallel cascade face detector. We define context image illumination and distinguish context using unsupervised learning. Many unsupervised method is available to distinguish varying illuminant images. This approach can be distribution face image and we can make the classifier for face image context. Therefore, in this paper, we parallel classifier that is strutted cascade classifier of two methods. In first classifier, we use sub-sampling feature extraction and in second classifier we use wavelet transformation method. We achieved very encouraging experimental results. Our method is enhancement varying illumination environment. [ABSTRACT FROM AUTHOR]

This paper considers the capacities of the elements in a set E efficiently so that the total cost for the increment of capacity can be decrease to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound. The paper supposes the cost w is a fuzzy variable. Network bottleneck capacity expansion problem with fuzzy cost is originally formulated as Expected value model according to some criteria. For solving the fuzzy model efficiently, network bottleneck capacity algorithm, fuzzy simulation, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the global exponential stability of Takagi-Sugeno Fuzzy cellular neural networks with time-varying delays. Using the reduction to absurdity, a less conservative delay-independent stability criterion is derived to guarantee the exponential stability of Takagi-Sugeno Fuzzy cellular neural networks with time-varying delays. Since our model is more general than some existing works, the results presented in this paper are the improvement and extension of the existed ones. [ABSTRACT FROM AUTHOR]

This paper presents a simple and practical solution to the 6-point 2-view focal-length estimation problem. Based on the hidden-variable technique we have derived a 15th degree polynomial in the unknown focal-length. During this course, a simple and constructive algorithm is established. To make use of multiple redundant measurements and then select the best solution, we suggest a kernel-voting scheme. The algorithm has been tested on both synthetic data and real images. Satisfactory results are obtained for both cases. For reference purpose we include our Matlab implementation in the paper, which is quite concise, consisting of 20 lines of code only. The result of this paper will make a small but useful module in many computer vision systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using specific descriptors that work for certain kinds of textures, but is rather based on a more generic approach that tries to adaptively capture the core properties of textures. It exploits the fundamental description of textures as images derived from stationary random fields and models the associated higher-order statistics nonparametrically. This general formulation enables the method to easily adapt to various kinds of textures. The method minimizes an entropy-based metric on the probability density functions of image neighborhoods to give an optimal segmentation. The entropy minimization drives a very fast level-set scheme that uses threshold dynamics, which allows for a very rapid evolution towards the optimal segmentation during the initial iterations. The method does not rely on a training stage and, hence, is unsupervised. It automatically tunes its important internal parameters based on the information content of the data. The method generalizes in a straightforward manner from the two-region case to an arbitrary number of regions and incorporates an efficient multi-phase level-set framework. This paper presents numerous results, for both the two-texture and multiple-texture cases, using synthetic and real images that include electron-microscopy images. [ABSTRACT FROM AUTHOR]

One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Random Fields (MRF's), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods. Unfortunately, most papers define their own energy function, which is minimized with a specific algorithm of their choice. As a result, the tradeoffs among different energy minimization algorithms are not well understood. In this paper we describe a set of energy minimization benchmarks, which we use to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods—graph cuts, LBP, and tree-reweighted message passing—as well as the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching and interactive segmentation. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods with minimal overhead. We expect that the availability of our benchmarks and interface will make it significantly easier for vision researchers to adopt the best method for their specific problems. Benchmarks, code, results and images are available at http://vision.middlebury.edu/MRF. [ABSTRACT FROM AUTHOR]

How face pose (rotating from right to left) influence the fusion authentication accuracy in face and fingerprint identity authentication system? This paper firstly tries to answer this question. The maximum rotating degree that fusion system can bear is given out by experiment. Furthermore, theoretical analysis deals with how face pose influence the fusion performance is proposed in this paper. Experiment results show that faces with big rotated degree can not be helpful but harmful to fusion system. And the maximum rotated angle of face that fusion system can bear is 20 degree. On the other hand, theoretical analysis proved that the mathematical inherence of influence of face pose on fusion system is not only the reduction of variance but also the decrease of distance between the genuine and imposter classes. [ABSTRACT FROM AUTHOR]

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance, we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance. [ABSTRACT FROM AUTHOR]

The paper presents the application of the wavelet transform of the frequency histogram of connected elements to the detection of very thin cracks in used pallets. First, the paper presents this novel concept and introduces the parameters that define a connected element, showing that the conventional grayscale intensity histogram of a digital image is a particular case of the histogram of connected elements. Then, the discriminant capability of the wavelet transform of this generalized histogram is analyzed. In particular, the information conveyed by the histogram of connected elements is exploited to detect very thin cracks in used pallets. An artificial neural network classifier to discriminate sound wood from defective wood with very thin cracks has been designed. The exhaustive experimental test carried out with numerous boards of used pallets has validated the proposed method, in particular its remarkably low ratio of false alarms. [ABSTRACT FROM AUTHOR]

This paper presents and application of Genetic Programming (GP) for time series forecast. Although this kind of application has been carried out with a wide range of techniques and with very good results, this paper presents a different approach. In most of the experiments done in time series forecasting the objective is, from a consecutive set of samples or time interval, to obtain the value of the sample in the next time step. The aim of this paper is to study the forecasting not only on the next sample, but in general several samples forward. This will allow the building of more complete prediction systems. With this objective, one of the most widely used series for this kind of application has been used, the Mackey-Glass series. [ABSTRACT FROM AUTHOR]

As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several methods to construct the ensemble. In this paper we present some new results in a comparison of twenty different methods. We have trained ensembles of 3, 9, 20 and 40 networks to show results in a wide spectrum of values. The results show that the improvement in performance above 9 networks in the ensemble depends on the method but it is usually low. Also, the best method for a ensemble of 3 networks is called "Decorrelated" and uses a penalty term in the usual Backpropagation function to decorrelate the network outputs in the ensemble. For the case of 9 and 20 networks the best method is conservative boosting. And finally for 40 networks the best method is Cels. [ABSTRACT FROM AUTHOR]

A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vector of the optimal Decision Boundary in terms of the Structure Risk Minimization principle. We also use a simple method to prove a property of Support Vector Machine (SVM) algorithm, which is combined with the optimal Decision Boundary Feature matrix to make our method consistent with the Kernel Fisher method(KFD). Moreover, KDBA is easily used in its applications, and the traditional Decision Boundary Analysis implementations are computationally expensive and sensitive to the size of the problem. Text classification problem is first used to testify the effectiveness of KDBA. Then experiments on the large-scale face database, the CAS-PEAL database, have illustrated its excellent performance compared with some popular face recognition methods such as Eigenface, Fisherface, and KFD. Keywords: Face Recognition, Kernel Fisher, Support Vector Machine [ABSTRACT FROM AUTHOR]

This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e. when 6 months have passed between recording of the gallery and the probe sets. We show how recognition rates fall significantly when data is captured between lengthy time intevals, for static and dynamic gait features. Under the assumption that it is possible to have some subjects from the probe for training and that similar subjects have similar changes in gait over time, a predictive model of changes in gait is suggested in this paper, which can improve the recognition capability. A small number of subjects were used for training and a much large number for classification and the probe contains the covariate data for a smaller number of subjects. Our new predictive model derives high recognition rates for different features which is a considerable improvement on recognition capability without this new approach. [ABSTRACT FROM AUTHOR]

A novel immunotronic approach to fault detection in hardware based on symbiotic evolution is proposed in this paper. In the immunotronic system, the generation of tolerance conditions corresponds to the generation of antibodies in the biological immune system. In this paper, the principle of antibody diversity, one of the most important concepts in the biological immune system, is employed and it is realized through symbiotic evolution. Symbiotic evolution imitates the generation of antibodies in the biological immune system more than the standard genetic algorithm(SGA) does. It is demonstrated that the suggested method outperforms the previous immunotronic methods with less running time. The suggested method is applied to fault detection in a decade counter (typical example of finite state machines) and MCNC finite state machines and its effectiveness is demonstrated by the computer simulation. Keywords: immunotronic system, hardware fault detection, tolerance conditions, antibody diversity, symbiotic evolution. [ABSTRACT FROM AUTHOR]

This paper is concerned with time series of graphs and compares two novel schemes that are able to predict the presence or absence of nodes in a graph. Our work is motivated by applications in computer network monitoring. However, the proposed prediction methods are generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved. [ABSTRACT FROM AUTHOR]

The shapes occurring in the images are essential features in image classification and retrieval. Due to their compactness and classification accuracy, Fourier-based shape descriptors are popular boundary-based methods for shape description. However, in the case of short boundary functions, the frequency resolution of the Fourier spectrum is low, which yields to inadequate shape description. Therefore, we have applied zero-padding method for the short boundary functions to improve their Fourier-based shape description. In this paper, we show that using this method the Fourier-based shape classification can be significantly improved. [ABSTRACT FROM AUTHOR]

The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image which is noisy. The "inverse" of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. We propose a chaotic model of Pattern Recognition (PR) for the theory of "blurring". The paper, which is an extension to a Companion paper [3] demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the Companion paper in which the chaotic PR system extracts the pattern from the input, this paper shows that the perception can be "blurred" if the dynamics of the chaotic system are modified. We thus propose a formal model, the Mb-AdNN, and present a rigorous analysis using the Routh-Hurwitz criterion and Lyapunov exponents. We also demonstrate, experimentally, the validity of our model by using a numeral dataset. [ABSTRACT FROM AUTHOR]

In this paper we describe a study carried out with SenToy: a tangible interface that has the shape of a doll and is used to capture emotions from its user whilst performing specific gestures. SenToy was used with an application named Fearnot!, which is a virtual storytelling environment, where characters act autonomously and in character, so that stories emerge from those autonomous actions. The integration of SenToy in FearNot! was evaluated in two ways: (1) if users were able to manipulate the tangible interface appropriately, even if engaged in storytelling situation and (2) if the emotions expressed by the users with SenToy were the same as the ones reported to have been felt after the session. The results of the study show that Sentoy can be used to understand how the viewers reacted to the stories portrayed, and at the same time that the emotions that were expressed with SenToy (apart from some exceptions, reported in the paper) are indeed similar as the ones reported to have been felt by the users. Keywords: Affective tangible interface, affective computing, synthetic characters. [ABSTRACT FROM AUTHOR]

Reticular Activating system which has a form of small neural networks in the brain is closely related system with the automatic nervous system. It takes charge of the function that distinguishes between memorizing one and the others, accepts the only selected information and discards the unnecessary things.In this paper, we propose Reticular Activating system which has Knowledge acquisition, selection, storing, reconfiguration and retrieving part. In this paper, On/Off Switching mechanism for flexible memory is specially designed and tested. [ABSTRACT FROM AUTHOR]

Human emotion is related to stimulus and cognitive appraisal. Emotion is very important to entertainment application of virtual reality. Emotion model of 3D virtual characters is a challenging branch of Intelligent Virtual Environment (IVE). A believable 3D character should be provided with emotion and perception. In general, a virtual character is regarded as an autonomous agent with sense, perception, behavior and action. An emotion model of 3D virtual characters on the basis of psychology theory is presented in this paper. Our work is to construct 3D virtual characters that have internal sensor and perception for external stimulus, and express emotion autonomously in real time. Firstly, architecture of a virtual character is set up by cognitive model; Secondly, emotion class is set up by OCC and Plutchik's emotion theory; Thirdly, some new concepts about emotion are presented with a general mathematical model which is relation among emotion, stimulus, motivation variable, personality variable. Fourthly, a perception model of 3D characters by Gibson's theory is introduced. As a result, an emotional animation demo system of 3D virtual character is implemented on PC. [ABSTRACT FROM AUTHOR]

The paper first builds a novel affective model based on rough sets, presents the static description of affective space. Meanwhile, the paper creatively combines rough sets with Markov chain, gives the dynamic forecast of human affective change. In this affective model, some concepts and states are defined such as affective description precision and so on. It is a fundamental work to more research. Simulations are done using Matlab software, and simulation results show that this affective model can well simulate the human emotion. The results of this paper are innovative. It is a new research direction of affective computing that rough sets and affective computing infiltrate each other. [ABSTRACT FROM AUTHOR]

In this paper, combined features of global and time-sequence were used as the characteristic parameters for speech emotional recognition. A new method based on formula of MMD (Modified Mahalanobis Distance) was proposed to decrease the estimated errors and simplify the calculation. Four emotions including happiness, anger, surprise and sadness are considered in the paper. 1000 recognizing sentences collected from 10 speakers were used to demonstrate the effectiveness of the new method. The average emotion recognition rate reached at 95%. Comparison with method of MQDF [1] (Modified quadratic discriminant function), Data analysis also displayed that the MMD is better than MQDF. [ABSTRACT FROM AUTHOR]

In this paper we propose a method based on a graph-theoretical cluster analysis for automatically finding and classifying clusters of microcalcifications in mammographic images, starting from the output of a microcalcification detection phase. This method does not require the user to provide either the expected number of clusters or any threshold values, often with no clear physical meaning, as other algorithms do. The proposed approach has been tested on a standard database of 40 mammographic images and has demonstrated to be very effective, even when the detection phase gives rise to several false positives. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present an efficient distance between n-dimensional histograms. Some image classification or image retrieval techniques use the distance between histograms as a first step of the classification process. For this reason, some algorithms that find the distance between histograms have been proposed in the literature. Nevertheless, most of this research has been applied on one-dimensional histograms due to the computation of a distance between multi-dimensional histograms is very expensive. In this paper, we present an efficient method to compare multi-dimensional histograms in O(2z), where z represents the number of bins. Results show a huge reduction of the time consuming with no recognition-ratio reduction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a block-based real-time people counting system that can be used in various environments including shopping mall entrances, elevators and escalators. The main contributions of this paper are robust background subtraction, the block-based decision method and real-time processing. For robust background subtraction obtained from a number of image sequences, we used a mixture of K Gaussian. The block-based decision method was used to determine the size of the given objects (moving people) in each block. We divided the images into 72 blocks and trained the mean and variance values of the specific objects in each block. This was done in order to provide real-time processing for up to 4 channels. Finally, we analyzed various actions that can occur with moving people in real world environments. [ABSTRACT FROM AUTHOR]

We present the ubiquitous intelligent sensing system for a smart home in this paper. A smart home is intelligent space that studies patterns of home contexts that is acquired in a home, and provides automatic home services for the human. The ubiquitous intelligent sensing system acquires seven sensing contexts from four sensor devices. We utilize association rules of data mining and linear support machine to analyze context patterns of seven contexts. Also, we analyze stress rates of the human through the HRV pattern of the ECG. If the human is suffering from stress, the ubiquitous intelligent sensing system provides home service to reduce one's stress. In this paper, we present the architecture and algorithms of the ubiquitous intelligent sensing system. We present the management toolkit to control the ubiquitous intelligent sensing system, and show implementation results of the smart home using the ubiquitous intelligent sensing system. [ABSTRACT FROM AUTHOR]

This paper describes a feature extraction method for realtime surveillance. Eigenspace models are a convenient way to represent set of images with widespread applications. In the traditional approach to calculate these eigenspace models, known as batch PCA method, model must capture all the images needed to build the internal representation. This approach has some drawbacks. Since the entire set of images is necessary, it is impossible to make the model build an internal representation while exploring a new person. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. In this paper we propose a method that allows for incremental eigenspace update method by incremental kernel PCA for realtime surveillance. Experimental results indicate that accuracy of proposed method is comparable to batch KPCA and outperforms than APEX. Furthermore proposed method has efficiency in memory requirement compared to KPCA. [ABSTRACT FROM AUTHOR]