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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 17B, Issue 6 - Dec 2010
Volume 17B, Issue 5 - Oct 2010
Volume 17B, Issue 4 - Aug 2010
Volume 17B, Issue 3 - Jun 2010
Volume 17B, Issue 2 - Apr 2010
Volume 17B, Issue 1 - Feb 2010
Selecting the target year
Detection of Music Mood for Context-aware Music Recommendation
Lee, Jong-In ; Yeo, Dong-Gyu ; Kim, Byeong-Man ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 263~274
DOI : 10.3745/KIPSTA.2010.17B.4.263
To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.
High-Capacity Reversible Watermarking through Predicted Error Expansion and Error Estimation Compensation
Lee, Hae-Yeoun ; Kim, Kyung-Su ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 275~286
DOI : 10.3745/KIPSTA.2010.17B.4.275
Reversible watermarking which can preserve the original quality of the digital contents and protect the copyright has been studied actively. Especially, in medical, military, and art fields, the need for reversible watermarking is increasing. This paper proposes a high-capacity reversible watermarking through predicted error expansion and error estimation compensation. Watermark is embedded by expanding the difference histogram between the original value and the predicted value. Differently from previous methods calculating the difference between adjacent pixels, the presented method calculates the difference between the original value and the predicted value, and that increases the number of the histogram value, where the watermark is embedded. As a result, the high capacity is achieved. The inserted watermark is extracted by restoring the histogram between the original value and the predicted value. To prove the performance, the presented algorithm is compared with other previous methods on various test images. The result supports that the presented algorithm has a perfect reversibility, a high image quality, and a high capacity.
3D Fingertip Estimation based on the TOF Camera for Virtual Touch Screen System
Kim, Min-Wook ; Ahn, Yang-Keun ; Jung, Kwang-Mo ; Lee, Chil-Woo ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 287~294
DOI : 10.3745/KIPSTB.2010.17B.4.287
TOF technique is one of the skills that can obtain the object's 3D depth information. But depth image has low resolution and fingertip occupy very small region, so, it is difficult to find the precise fingertip's 3D information by only using depth image from TOF camera. In this paper, we estimate fingertip's 3D location using Arm Model and reliable hand's 3D location information that is modified by hexahedron as hand model. Using proposed method we can obtain more precise fingertip's 3D information than using only depth image.
Example-based Super Resolution Text Image Reconstruction Using Image Observation Model
Park, Gyu-Ro ; Kim, In-Jung ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 295~302
DOI : 10.3745/KIPSTB.2010.17B.4.295
Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.
Medical Image Registration by Combining Gradient Vector Flow and Conditional Entropy Measure
Lee, Myung-Eun ; Kim, Soo-Hyung ; Kim, Sun-Worl ; Lim, Jun-Sik ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 303~308
DOI : 10.3745/KIPSTB.2010.17B.4.303
In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.
Model based Facial Expression Recognition using New Feature Space
Kim, Jin-Ok ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 309~316
DOI : 10.3745/KIPSTB.2010.17B.4.309
This paper introduces a new model based method for facial expression recognition that uses facial grid angles as feature space. In order to be able to recognize the six main facial expression, proposed method uses a grid approach and therefore it establishes a new feature space based on the angles that each gird's edge and vertex form. The way taken in the paper is robust against several affine transformations such as translation, rotation, and scaling which in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Also, this paper demonstrates the process that the feature space is created using angles and how a selection process of feature subset within this space is applied with Wrapper approach. Selected features are classified by SVM, 3-NN classifier and classification results are validated with two-tier cross validation. Proposed method shows 94% classification result and feature selection algorithm improves results by up to 10% over the full set of feature.
Semantic Web-based Clinical Decision Support System for Armed Forces Hospitals
Yoo, Dong-Hee ; Ra, Min-Young ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 317~326
DOI : 10.3745/KIPSTB.2010.17B.4.317
To improve the diagnosis and prescription for military personnel, it is required to adopt Clinical Decision Support System (CDSS) in armed forces hospitals. The objective of this paper is to suggest a CDSS for armed forces hospitals using semantic web technologies. To this end, we designed military medical ontologies and military medical rules which consist of the various concepts and rules for supporting medical activities. We developed a semantic web-based CDSS to demonstrate the use of the ontologies and rules for treating military patients. We also showed the process of semantic search for the medical records which are created from the semantic web-based CDSS.
Question Classification Based on Word Association for Question and Answer Archives
Jin, Xueying ; Lee, Kyung-Soon ;
The KIPS Transactions:PartB, volume 17B, issue 4, 2010, Pages 327~332
DOI : 10.3745/KIPSTB.2010.17B.4.327
Word mismatch is the most significant problem that causes low performance in question classification, whose questions consist of only two or three words that expressed in many different ways. So, it is necessary to apply word association in question classification. In this paper, we propose question classification method using translation-based language model, which use word translation probabilities for question-question pair that is learned in the same category. In the experiment, we prove that translation probabilities of question-question pairs in the same category is more effective than question-answer pairs in total collection.