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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 13B, Issue 7 - Dec 2006
Volume 13B, Issue 6 - Dec 2006
Volume 13B, Issue 5 - Oct 2006
Volume 13B, Issue 4 - Aug 2006
Volume 13B, Issue 3 - Jun 2006
Volume 13B, Issue 2 - Apr 2006
Volume 13B, Issue 1 - Feb 2006
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Probe Classification of an On-Off Type DNA Chip Using Template Matching Method
Ryu, Mun-Ho ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 579~584
DOI : 10.3745/KIPSTB.2006.13B.6.579
This paper proposes a nonlinear template matching measure, called counting measure, as a signal detection measure that is defined as the number of on pixels in the spot area. It is applied to classify probes for an on-off type DNA chip, where each probe spot is classified as hybridized or not. The counting measure also incorporates the maximum response search method, where the expected signal is obtained by taking the maximum among the measured responses of the various positions and sizes of the spot template. The counting measure was compared to existing signal detection measures such as the normalized correlation and the median for 2390 patient samples tested on the human papiliomavirus (HPV) DNA chip. The counting measure performed the best regardless of whether or not the maximum response search method was used. The experimental results showed that the counting measure combined with the positional search was the most preferable.
Motion Estimation by Classification of Block Types
Yoon Hyo-Sun ; Yoo Jae-Myeong ; Park Mi-Seon ; Kim Mi-Young ; Toan Nguyen Dinh ; Lee Guee-Sang ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 585~590
DOI : 10.3745/KIPSTB.2006.13B.6.585
Although motion estimation Plays an important role in digital video compression, complex search procedure is required to find an optimal motion vector. To reduce the search time, the search start point should be set up properly md efficient search pattern is needed. If the overall motion of the torrent block can be predicted, motion estimation can be performed efficiently. In this paper. block types are classified using candidate vectors and the motion activity of the block is predicted which leads to the search start point close to the optimal motion vector. The proposed method proves to be about 1.5
7 times faster than existing methods with about 0.02
0.2(dB) improvement of picture quality in images with large movements.
Word Image Decomposition from Image Regions in Document Images using Statistical Analyses
Jeong, Chang-Bu ; Kim, Soo-Hyung ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 591~600
DOI : 10.3745/KIPSTB.2006.13B.6.591
This paper describes the development and implementation of a algorithm to decompose word images from image regions mixed text/graphics in document images using statistical analyses. To decompose word images from image regions, the character components need to be separated from graphic components. For this process, we propose a method to separate them with an analysis of box-plot using a statistics of structural components. An accuracy of this method is not sensitive to the changes of images because the criterion of separation is defined by the statistics of components. And then the character regions are determined by analyzing a local crowdedness of the separated character components. finally, we devide the character regions into text lines and word images using projection profile analysis, gap clustering, special symbol detection, etc. The proposed system could reduce the influence resulted from the changes of images because it uses the criterion based on the statistics of image regions. Also, we made an experiment with the proposed method in document image processing system for keyword spotting and showed the necessity of studying for the proposed method.
Liver Tumor Detection Using Texture PCA of CT Images
Sur, Hyung-Soo ; Chong, Min-Young ; Lee, Chil-Woo ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 601~606
DOI : 10.3745/KIPSTB.2006.13B.6.601
The image data amount that used in medical institution with great development of medical technology is increasing rapidly. Therefore, people need automation method that use image processing description than macrography of doctors for analysis many medical image. In this paper. we propose that acquire texture information to using GLCM about liver area of abdomen CT image, and automatically detects liver tumor using PCA from this data. Method by one feature as intensity of existent liver humor detection was most but we changed into 4 principal component accumulation images using GLCM`s texture information 8 feature. Experiment result, 4 principal component accumulation image`s variance percentage is 89.9%. It was seen this compare with liver tumor detecting that use only intensity about 92%. This means that can detect liver tumor even if reduce from dimension of image data to 4 dimensions that is the half in 8 dimensions.
Design and Implementation of Error Concealment Algorithm using Data Hiding and Adaptive Selection of Adjacent Motion Vectors
Lee, Hyun-Woo ; Seong, Dong-Su ; Lee, Keon-Bae ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 607~614
DOI : 10.3745/KIPSTB.2006.13B.6.607
In this paper, we propose an error resilience video coder which uses a hybrid error concealment algorithm. Firstly, the algorithm uses the error concealment with data hiding. If the hiding information is lost, the motion vector of lost macroblock is computed with adaptive selection of adjacent motion vectors and OBMC (Overlapped Block Motion Compensation) is applied with this motion vector. We know our algorithm is more effective in case of continuous GOB. The results show more significant improvement than many temporal concealment methods such as MVRI (Motion Vector Rational Interpolation) or existing error concealment using data hiding.
The Parallel ANN(Artificial Neural Network) Simulator using Mobile Agent
Cho, Yong-Man ; Kang, Tae-Won ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 615~624
DOI : 10.3745/KIPSTB.2006.13B.6.615
The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN`s execution on virtual parallel computer is similar to that of ANN`s execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.
Nearest-Neighbor Collaborative Filtering Using Dimensionality Reduction by Non-negative Matrix Factorization
Ko, Su-Jeong ;
The KIPS Transactions:PartB, volume 13B, issue 6, 2006, Pages 625~632
DOI : 10.3745/KIPSTB.2006.13B.6.625
Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn`t rate by a new user based on the values that the nearest neighbors rated items.