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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 18B, Issue 6 - Dec 2011
Volume 18B, Issue 5 - Oct 2011
Volume 18B, Issue 4 - Aug 2011
Volume 18B, Issue 3 - Jun 2011
Volume 18B, Issue 2 - Apr 2011
Volume 18B, Issue 1 - Feb 2011
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Acquisition of Intrinsic Image by Omnidirectional Projection of ROI and Translation of White Patch on the X-chromaticity Space
Kim, Dal-Hyoun ; Hwang, Dong-Guk ; Lee, Woo-Ram ; Jun, Byoung-Min ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 51~56
DOI : 10.3745/KIPSTB.2011.18B.2.051
Algorithms for intrinsic images reduce color differences in RGB images caused by the temperature of black-body radiators. Based on the reference light and detecting single invariant direction, these algorithms are weak in real images which can have multiple invariant directions when the scene illuminant is a colored illuminant. To solve these problems, this paper proposes a method of acquiring an intrinsic image by omnidirectional projection of an ROI and a translation of white patch in the
-chromaticity space. Because it is not easy to analyze an image in the three-dimensional RGB space, the
-chromaticity is also employed without the brightness factor in this paper. After the effect of the colored illuminant is decreased by a translation of white patch, an invariant direction is detected by omnidirectional projection of an ROI in this chromaticity space. In case the RGB image has multiple invariant directions, only one ROI is selected with the bin, which has the highest frequency in 3D histogram. And then the two operations, projection and inverse transformation, make intrinsic image acquired. In the experiments, test images were four datasets presented by Ebner and evaluation methods was the follows: standard deviation of the invariant direction, the constancy measure, the color space measure and the color constancy measure. The experimental results showed that the proposed method had lower standard deviation than the entropy, that its performance was two times higher than the compared algorithm.
Automatic Left Ventricle Segmentation Algorithm using K-mean Clustering and Graph Searching on Cardiac MRI
Jo, Hyun-Wu ; Lee, Hae-Yeoun ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 57~66
DOI : 10.3745/KIPSTB.2011.18B.2.057
To prevent cardiac diseases, quantifying cardiac function is important in routine clinical practice by analyzing blood volume and ejection fraction. These works have been manually performed and hence it requires computational costs and varies depending on the operator. In this paper, an automatic left ventricle segmentation algorithm is presented to segment left ventricle on cardiac magnetic resonance images. After coil sensitivity of MRI images is compensated, a K-mean clustering scheme is applied to segment blood area. A graph searching scheme is employed to correct the segmentation error from coil distortions and noises. Using cardiac MRI images from 38 subjects, the presented algorithm is performed to calculate blood volume and ejection fraction and compared with those of manual contouring by experts and GE MASS software. Based on the results, the presented algorithm achieves the average accuracy of 6.2mL
3.0 and 2.1%
1.5 in diastolic phase, systolic phase and ejection fraction, respectively. Moreover, the presented algorithm minimizes user intervention rates which was critical to automatize algorithms in previous researches.
Cluster Analysis of SNPs with Entropy Distance and Prediction of Asthma Type Using SVM
Lee, Jung-Seob ; Shin, Ki-Seob ; Wee, Kyu-Bum ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 67~72
DOI : 10.3745/KIPSTB.2011.18B.2.067
Single nucleotide polymorphisms (SNPs) are a very important tool for the study of human genome structure. Cluster analysis of the large amount of gene expression data is useful for identifying biologically relevant groups of genes and for generating networks of gene-gene interactions. In this paper we compared the clusters of SNPs within asthma group and normal control group obtained by using hierarchical cluster analysis method with entropy distance. It appears that the 5-cluster collections of the two groups are significantly different. We searched the best set of SNPs that are useful for diagnosing the two types of asthma using representative SNPs of the clusters of the asthma group. Here support vector machines are used to evaluate the prediction accuracy of the selected combinations. The best combination model turns out to be the five-locus SNPs including one on the gene ALOX12 and their accuracy in predicting aspirin tolerant asthma disease risk among asthmatic patients is 66.41%.
S-MINE Algorithm for the TSP
Hwang, Sook-Hi ; Weon, Il-Yong ; Ko, Sung-Bum ; Lee, Chang-Hoon ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 73~82
DOI : 10.3745/KIPSTB.2011.18B.2.073
There are a lot of people trying to solve the Traveling Salesman Problem (TSP) by using the Meta Heuristic Algorithms. TSP is an NP-Hard problem, and is used in testing search algorithms and optimization algorithms. Also TSP is one of the models of social problems. Many methods are proposed like Hybrid methods and Custom-built methods in Meta Heuristic. In this paper, we propose the S-MINE Algorithm to use the MINE Algorithm introduced in 2009 on the TSP.
A Smart Script System for Implementing Intelligent Behaviors of Mobile Personal Assistants
Kim, In-Cheol ; Oh, Hui-Kyoung ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 83~86
DOI : 10.3745/KIPSTB.2011.18B.2.083
In this paper, we present the plan execution model for dynamic mobile computing environments, and then introduce the smart script system developed on these base models. The smart script system includes the smart script language, in which the task knowledge of a mobile personal assistant is represented, and the script execution engine, by which the scripts are dynamically executed in response to the given task goal and the environmental changes. In order to evaluate the utility and the performance of our system, we implement an application service called Smart Reservation and conduct some experiments.
Transliteration Correction Method using Korean Alphabet Viable Prefix
Kwon, Soon-Ho ; Kwon, Hyuk-Chul ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 87~92
DOI : 10.3745/KIPSTB.2011.18B.2.087
In Korean documents, there are diverse spellings of transliterated foreign loanwords. This fact diminishes the performance of information retrieval systems in that a foreign word can be recognized differently, which is to say, as two or several different words. Thus, information retrieval systems require preprocessing to correct nonstandard loanword spellings prior to searching and recognizing corresponding equivalent words. This paper proposes a method that improves precision and processing efficiency using the Korean alphabet's viable prefix, which prunes a virtual tree from which candidate loanwords are created.
Korean Document Classification Using Extended Vector Space Model
Lee, Samuel Sang-Kon ;
The KIPS Transactions:PartB, volume 18B, issue 2, 2011, Pages 93~108
DOI : 10.3745/KIPSTB.2011.18B.2.093
We propose a extended vector space model by using ambiguous words and disambiguous words to improve the result of a Korean document classification method. In this paper we study the precision enhancement of vector space model and we propose a new axis that represents a weight value. Conventional classification methods without the weight value had some problems in vector comparison. We define a word which has same axis of the weight value as ambiguous word after calculating a mutual information value between a term and its classification field. We define a word which is disambiguous with ambiguous meaning as disambiguous word. We decide the strengthness of a disambiguous word among several words which is occurring ambiguous word and a same document. Finally, we proposed a new classification method based on extension of vector dimension with ambiguous and disambiguous words.