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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
Selecting the target year
An improvement in FGS coding scheme for high quality scalability
Boo, Hee-Hyung ; Kim, Sung-Ho ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 249~254
DOI : 10.3745/KIPSTB.2011.18B.5.249
FGS (fine granularity scalability) supporting scalability in MPEG-4 Part 2 is a scalable video coding scheme that provides bit-rate adaptation to varying network bandwidth thereby achieving of its optimal video quality. In this paper, we proposed FGS coding scheme which performs one more bit-plane coding for residue signal occured in the enhancement-layer of the basic FGS coding scheme. The experiment evaluated in terms of video quality scalability of the proposed FGS coding scheme by comparing with FGS coding scheme of the MPEG-4 verification model (VM-FGS). The comparison was conducted by analysis of PSNR values of three tested video sequences. The results showed that when using rate control algorithm VM5+, the proposed FGS coding scheme obtained Y, U, V PSNR of 0.4 dB, 9.4 dB, 9 dB averagely higher and when using fixed QP value 17, obtained Y, U, V PSNR of 4.61 dB, 20.21 dB, 16.56 dB averagely higher than the existing VM-FGS. From results, we found that the proposed FGS coding scheme has higher video quality scalability to be able to achieve video quality from minimum to maximum than VM-FGS.
Forward Vehicle Detection Algorithm Using Column Detection and Bird's-Eye View Mapping Based on Stereo Vision
Lee, Chung-Hee ; Lim, Young-Chul ; Kwon, Soon ; Kim, Jong-Hwan ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 255~264
DOI : 10.3745/KIPSTB.2011.18B.5.255
In this paper, we propose a forward vehicle detection algorithm using column detection and bird's-eye view mapping based on stereo vision. The algorithm can detect forward vehicles robustly in real complex traffic situations. The algorithm consists of the three steps, namely road feature-based column detection, bird's-eye view mapping-based obstacle segmentation, obstacle area remerging and vehicle verification. First, we extract a road feature using maximum frequent values in v-disparity map. And we perform a column detection using the road feature as a new criterion. The road feature is more appropriate criterion than the median value because it is not affected by a road traffic situation, for example the changing of obstacle size or the number of obstacles. But there are still multiple obstacles in the obstacle areas. Thus, we perform a bird's-eye view mapping-based obstacle segmentation to divide obstacle accurately. We can segment obstacle easily because a bird's-eye view mapping can represent the position of obstacle on planar plane using depth map and camera information. Additionally, we perform obstacle area remerging processing because a segmented obstacle area may be same obstacle. Finally, we verify the obstacles whether those are vehicles or not using a depth map and gray image. We conduct experiments to prove the vehicle detection performance by applying our algorithm to real complex traffic situations.
Intensity Correction of 3D Stereoscopic Images Using Binarization-Based Region Segmentation
Kim, Sang-Hyun ; Kim, Jeong-Yeop ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 265~270
DOI : 10.3745/KIPSTB.2011.18B.5.265
In this paper, we propose a method for intensity correction using binarization-based region segmentation in 3D stereoscopic images. In the proposed method, 3D stereoscopic right image is segmented using binarizarion. Small regions in the segmented image are eliminated. For each region in right image, a corresponding region in left image is decided through region matching using correlation coefficient. When region-based matching, in order to prevent overlap between regions, we remove a portion of the area closed to the region boundary using morphological filter. The intensity correction in left and right image can be performed through histogram specification between the corresponding regions. Simulation results show the proposed method has the smallest matching error than the conventional method when we generate the right image from the left image using block based motion compensation.
Performance Comparison between Neural Network Model and Statistical Model for Prediction of Damage Cost from Storm and Flood
Choi, Seon-Hwa ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 271~278
DOI : 10.3745/KIPSTB.2011.18B.5.271
Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.
Contents Recommendation Method Based on Social Network
Pei, Yun-Feng ; Sohn, Jong-Soo ; Chung, In-Jeong ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 279~290
DOI : 10.3745/KIPSTB.2011.18B.5.279
As the volume of internet and web contents have shown an explosive growth in recent years, lately contents recommendation system (CRS) has emerged as an important issue. Consequently, researches on contents recommendation method (CRM) for CRS have been conducted consistently. However, traditional CRMs have the limitations in that they are incapable of utilizing in web 2.0 environments where positions of content creators are important. In this paper, we suggest a novel way to recommend web contents of high quality using both degree of centrality and TF-IDF. For this purpose, we analyze TF-IDF and degree of centrality after collecting RSS and FOAF. Then we recommend contents using these two analyzed values. For the verification of the suggested method, we have developed the CRS and showed the results of contents recommendation. With the suggested idea we can analyze relations between users and contents on the entered query, and can consequently provide the appropriate contents to the user. Moreover, the implemented system we suggested in this paper can provide more reliable contents than traditional CRS because the importance of the role of content creators is reflected in the new system.
Red Tide Blooms Prediction using Fuzzy Reasoning
Park, Sun ; Lee, Seong-Ro ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 291~294
DOI : 10.3745/KIPSTB.2011.18B.5.291
Red tide is a temporary natural phenomenon to change sea color by harmful algal blooms, which finfish and shellfish die en masse. There have been many studies on red tide due to increasing of harmful algae damage of fisheries in Korea. Particularly, red tide damage can be minimized by means of prediction of red tide blooms. However, the most of red tide research in Korea has been focused only classification of red tide which it is not enough for predicting red tide blooms. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning.
A Mobile Semantic Integrated Search System of National Defense Research Information
Yoo, Dong-Hee ; Ra, Min-Young ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 295~304
DOI : 10.3745/KIPSTB.2011.18B.5.295
To effectively manage research information in the field of national defense, metadata about the information should be managed systematically, and an integrated system to help convergence and management of the information should be implemented based on the metadata. In addition, the system should provide the users with effective integrated search services in a mobile environment, because searching via the use of mobile devices is increasing. The objective of this paper is to suggest a MSISS (Mobile Semantic Integrated Search System), which satisfies the requirements for effective management of the national defense research information. Specifically, we defined national defense research ontologies and national defense research rules after analyzing the Dublin Core metadata and database information of the major military research institutions. We implemented a prototype system for MSISS to demonstrate the use of the ontologies and rules for semantic integrated searching of the military research information. We also presented a triple-based search service to support semantic integrated search in a mobile environment and suggested future mobile semantic integrated search services.
A Study on Visual Perception based Emotion Recognition using Body-Activity Posture
Kim, Jin-Ok ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 305~314
DOI : 10.3745/KIPSTB.2011.18B.5.305
Research into the visual perception of human emotion to recognize an intention has traditionally focused on emotions of facial expression. Recently researchers have turned to the more challenging field of emotional expressions through body posture or activity. Proposed work approaches recognition of basic emotional categories from body postures using neural model applied visual perception of neurophysiology. In keeping with information processing models of the visual cortex, this work constructs a biologically plausible hierarchy of neural detectors, which can discriminate 6 basic emotional states from static views of associated body postures of activity. The proposed model, which is tolerant to parameter variations, presents its possibility by evaluating against human test subjects on a set of body postures of activities.
A Study on Spam Document Classification Method using Characteristics of Keyword Repetition
Lee, Seong-Jin ; Baik, Jong-Bum ; Han, Chung-Seok ; Lee, Soo-Won ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 315~324
DOI : 10.3745/KIPSTB.2011.18B.5.315
In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.
A Sentence Sentiment Classification reflecting Formal and Informal Vocabulary Information
Cho, Sang-Hyun ; Kang, Hang-Bong ;
The KIPS Transactions:PartB, volume 18B, issue 5, 2011, Pages 325~332
DOI : 10.3745/KIPSTB.2011.18B.5.325
Social Network Services(SNS) such as Twitter, Facebook and Myspace have gained popularity worldwide. Especially, sentiment analysis of SNS users' sentence is very important since it is very useful in the opinion mining. In this paper, we propose a new sentiment classification method of sentences which contains formal and informal vocabulary such as emoticons, and newly coined words. Previous methods used only formal vocabulary to classify sentiments of sentences. However, these methods are not quite effective because internet users use sentences that contain informal vocabulary. In addition, we construct suggest to construct domain sentiment vocabulary because the same word may represent different sentiments in different domains. Feature vectors are extracted from the sentiment vocabulary information and classified by Support Vector Machine(SVM). Our proposed method shows good performance in classification accuracy.