• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2080-2091
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• 1997

It is generally recognized that Video On Demand (VOD) service will become a promising interactive service in the emerging broadband integrated services digital networks. A centralized VOD system, all programs are stored in a single VOD server which is linked to each user via exchanges, is applicable when a small number of users enjoys the VOD service. However, in case of large service penetration, it is very important to solve the problems of bandwidth and load concentrating in the central video server(CVS) and program transmission network. In this paper, the architecture of the video distribution service network is studied, then a traffic characteristics and models for VOD system are established, and proposed program allocation method to video servers. For this purpose, we present an analysis of program storage amount in each LVS(Local Video Server), transmission traffic volume between LVSs, and link traffic volume between CVS and LVSs, according to changing the related factors such as demand, the number of LVS, vision probability, etc. A method for finding out storage capacity in LVSs is also presented on the basis of the tradeoffs among program storage cost, link traffic cost, and transmission cost.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.151-160
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• 2002

Cow image processing technique would be useful not only for recognizing an individual but also for establishing the image database and analyzing the shape of cows. A cow (Holstein) has usually the unique speckle pattern. In this study, the individual recognition of cow was carried out using the speckle pattern and the content-based image retrieval technique. Sixty cow images of 16 heads were captured under outdoor illumination, which were complicated images due to shadow, obstacles and walking posture of cow. Sixteen images were selected as the reference image for each cow and 44 query images were used for evaluating the efficiency of individual recognition by matching to each reference image. Run-lengths and positions of runs across speckle area were calculated from 40 horizontal line profiles for ROI (region of interest) in a cow body image after 3 passes of 5$\times$5 median filtering. A similarity measure for recognizing cow individuals was calculated using Euclidean distance of normalized G-frame histogram (GH). normalized speckle run-length (BRL), normalized x and y positions (BRX, BRY) of speckle runs. This study evaluated the efficiency of individual recognition of cow using Recall(Success rate) and AVRR(Average rank of relevant images). Success rate of individual recognition was 100% when GH, BRL, BRX and BRY were used as image query indices. It was concluded that the histogram as global property and the information of speckle runs as local properties were good image features for individual recognition and the developed system of individual recognition was reliable.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.53-60
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• 2009

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking; and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.603-607
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• 2004

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1129-1135
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• 2007

Human face detection technique plays an important role in computer vision area. It has lots of applications such as face recognition, video surveillance, human computer interface, face image database management, and querying image databases. In this paper, a fast face detection approach using Principal Component Analysis (PCA) and Support Vector Machines (SVM) is proposed based on the previous study on face detection technique. In the proposed detection system, firstly it filter the face potential area using statistical feature which is generated by analyzing the local histogram distribution the detection process is speeded up by eliminating most of the non-face area in this step. In the next step, PCA feature vectors are generated, and then detect whether there are faces present in the test image using SVM classifier. Finally, store the detection results and output the results on the test image. The test images in this paper are from CMU face database. The face and non-face samples are selected from the MIT data set. The experimental results indicate the proposed method has good performance for face detection.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.371-384
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• 2019

Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.396-402
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• 2021

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

• Journal of Technology Innovation
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• v.30 no.4
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• pp.29-56
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• 2022

This study is a review paper on the contents and policy direction of the 'Transformative regional innovation policy'. The transformative regional innovation policy is a policy that develops countermeasures against climate crisis, aging, and polarization with a vision of system transformation from the perspective of local residents. The structure of the study is as follows. First, it deals with the characteristics of transformative regional innovation policy theory, which is distinguished from existing regional innovation policies. Next, studies related to major elements of transformative regional innovation policies, types of system transformation, and industrial formation are reviewed. The elements that differentiate the transformative regional innovation policy from existing policies are summarized and issues to be addressed in order to develop future discussions are drawn.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.46-55
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• 2022

Eight years have passed since the Sponsoring Consortium for Open Access Publishing in Particle Physics (SCOAP3) was launched. SCOAP3 is one of the most successful global partnerships and funds for Open Access and has been benchmarked by other Open Access initiatives. The Korea Institute of Science and Technology Information (KISTI) joined as the first Asian partner in 2011, and has supported its shared vision and contributed its financial commitment since the beginning of SCOAP3. SCOAP3-Korea is the first bottom-up collaboration for local libraries to re-direct funds previously used for subscriptions to Open Access publishing. This paper explores the roles and responsibilities of KISTI in the Open Access quest. It describes the commitment to SCOAP3 in South Korea, including how the collaboration model for SCOAP3-Korea differs from the global model. This paper also discusses the impact of SCOAP3-Korea by analyzing publications affiliated by Korean authors in SCOAP3 journals for the last six years (2014-2019). We have integrated the national R&D project and research outcome data from NTIS (National Science and Technology Information Service) to investigate the research articles benefited by SCOAP3 and research publications in non-SCOAP3 journals. The positive impact of SCOAP3 in increasing research publication in the discipline was revealed compared to non-SCOAP3 journals. In addition, the financial benefit of SCOAP3-Korea has been proven. With regard to the investment for readers, $137,094 USD was saved during the SCOAP3 Phase 1 and 2, while $748,923 USD was saved with regard to publication fees. We discussed the lessons from SCOAP3-Korea for commitment to a larger-scale Open Access transition.