• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.200-204
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• 2008

In this paper, to solve localization problem for out-door navigation of robotic vehicles, a particle filter based 3D object recognition framework that can estimate the pose of a building or its entrance is presented. A particle filter framework of multiple evidence fusion and model matching in a sequence of images is presented for robust recognition and pose estimation of 3D objects. The proposed approach features 1) the automatic selection and collection of an optimal set of evidences 2) the derivation of multiple interpretations, as particles representing possible object poses in 3D space, and the assignment of their probabilities based on matching the object model with evidences, and 3) the particle filtering of interpretations in time with the additional evidences obtained from a sequence of images. The proposed approach has been validated by the stereo-camera based experimentation of 3D object recognition and pose estimation, where a combination of photometric and geometric features are used for evidences.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.775-778
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• 2008

Augmented Reality is a growing area in virtual reality research, The world environment around us provides a wealth of information that is difficult to duplicate in a computer. This evidenced by the worlds used in virtual environments. An augmented reality system generates a composite view for the user. It is a combination of the real scene viewed by the user and a virtual scene generated by the computer that augments the scene with addition information. The registration method represent to the user enhances that person's performance in and perception of the world. It decide the direction and location between real world and 3D graphic objects. The registration method devide two method, Model based tracking and Move-Matching. This paper researched at to generate a commerce correlation using a tracking object method, using at a color distribution and information, in the sequence scene.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3697-3705
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• 2011

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose a new prediction search algorithm for block matching using the temporal and spatial correlation of the video sequence and local statistics of neighboring motion vectors. The proposed ANBA(Adaptive Neighboring-Block Search Algorithm) determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vectors of neighboring blocks around the same block of the previous frame and the current frame and use a previous motion vector. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 1.06dB as depend on the video sequences and improved about 0.01~0.64dB over MVFAST and PMVFAST.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.41-50
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• 2005

A novel image mosaicking technique suitable for 3D urban visualization is proposed. It is not effective to apply 2D image mosaicking techniques for urban visualization when, for example, one is filming a sequence of images from a side-looking video camera along a road in an urban area. The proposed method presents the roadside scene captured by a side-looking video camera as a continuous set of textured planar faces, which are termed 'multiple planes' in this paper. The exterior parameters of each frame are first calculated through automatically selected matching feature points. The matching feature points are also used to estimate a plane approximation of the scene geometry for each frame. These planes are concatenated to create an approximate model on which images are back-projected as textures. Here, we demonstrate algorithm that creates efficient image mosaics in 3D space from a sequence of real images.

• Journal of the Korea Computer Industry Society
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• v.10 no.5
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• pp.227-234
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• 2009

The Paper introduces a new technique for block matching motion estimation. since the temporal correlation of a animation sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the scene change detection algorithm for block matching using the temporal correlation of the animation sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that the PSNR values are improved as high as 9~32% in terms of average number of search point per motion vector estimation and improved about 0.06~0.21dB on an average except the FS(full search) algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1070-1075
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• 2009

In this paper, we propose new depth estimation and intermediate view synthesis algorithms for three-dimensional video generation. In order to improve temporal consistency of the depth map sequence, we add a temporal weighting function to the conventional matching function when we compute the matching cost for estimating the depth information. In addition, we propose a boundary noise removal method in the view synthesis operation. after finding boundary noise areas using the depth map, we replace them with corresponding texture information from the other reference image. Experimental results showed that the proposed algorithm improved temporal consistency of the depth sequence and reduced flickering artifacts in the virtual view. It also improved visual quality of the synthesized virtual views by removing the boundary noise.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.341-346
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• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.43-51
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• 2006

This paper proposes a metrics for example matching under the example-based machine translation for English-Korean machine translation. Our metrics served as similarity measure is based on edit-distance algorithm, and it is employed to retrieve the most similar example sentences to a given query. Basically it makes use of simple information such as lemma and part-of-speech information of typographically mismatched words. Edit-distance algorithm cannot fully reflect the context of matched word units. In other words, only if matched word units are ordered, it is considered that the contribution of full matching context to similarity is identical to that of partial matching context for the sequence of words in which mismatching word units are intervened. To overcome this drawback, we propose the context-weighting scheme that uses the contiguity information of matched word units to catch the full context. To change the edit-distance metrics representing dissimilarity to similarity metrics, to apply this context-weighted metrics to the example matching problem and also to rank by similarity, we normalize it. In addition, we generalize previous methods using some linguistic information to one representative system. In order to verify the correctness of the proposed context-weighted metrics, we carry out the experiment to compare it with generalized previous methods.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• Microbiology and Biotechnology Letters
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• v.46 no.2
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• pp.162-170
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• 2018

Non-typhoidal Salmonella is one of the main pathogens causing food-borne illness in humans, with up to 20% of cases resulting from consumption of pork products. Over the gastroenteritis signs, multidrug resistant Salmonella has arisen. In this study, pan-susceptible phenotypic strains of Salmonella enterica serotype Weltevreden recovered from pig production chain in Chiang Mai, Thailand during 2012-2014 were chosen for analysis. The aim of this study was to use whole genome sequencing (WGS) data with an emphasis on antimicrobial resistance gene investigation to assess their pathogenic potential and genetic diversity determination based on whole genome Multilocus Sequence Typing (wgMLST) to expand epidemiological knowledge and to provide additional guidance for disease control. Analyis using ResFinder 3.0 for WGS database tracing found that one of pan-susceptible phenotypic strain carried five classes of resistance genes: aminoglycoside, beta-lactam, phenicol, sulfonamide, and tetracycline associated genes. Twenty four and 36 loci differences were detected by core genome Multilocus Sequence Typing (cgMLST) and pan genome Multilocus Sequence Typing (pgMLST), respectively, in two matching strains (44/13 vs A543057 and A543056 vs 204/13) initially assigned by conventional MLST and Pulsed-field Gel Electrophoresis (PFGE). One hundread percent discriminant ability can be achieved using the wgMLST technique. WGS is currently the ultimate molecular technique for various in-depth studies. As the findings stated above, a new of "gold standard typing method era" for routine works in genome study is being set.