• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.260-267
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• 2011

This paper proposes a method for segmenting objects from the background in IR(Infrared) images based on GrabCut algorithm. The GrabCut algorithm needs the window encompassing the interesting known object. This procedure is processed by user. However, to apply it for object recognition problems in image sequences. the location of window should be determined automatically. For this, we adopted the Otsu' algorithm for segmenting the interesting but unknown objects in an image coarsely. After applying the Otsu' algorithm, the window is located automatically by blob analysis. The GrabCut algorithm needs the probability distributions of both the candidate object region and the background region surrounding closely the object for estimating the Gaussian mixture models(GMMs) of the object and the background. The probability distribution of the background is computed from the background window, which has the same number of pixels within the candidate object region. Experiments for various IR images show that the proposed method is proper to segment out the interesting object in IR image sequences. To evaluate performance of proposed segmentation method, we compare other segmentation methods.

• Journal of KIISE:Software and Applications
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• v.29 no.6
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• pp.355-366
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• 2002

This paper introduces the MPEG-4 video frame-based bitrate control methodology using two dimensional history pool and a sliding window. Proposed method preferentially clusters the encoded results according to the image characteristics and stores those results into the 2 dimensional history pool. Among the stored results in the pool, the sliding window collects the encoded results whose characteristics are very similar to the image frame to be encoded. Feedback regression is finally carried out bated on the collected results. Therefore proposed method can actively adapt to the rapid varying image characteristics by reducing the occurrences of the extrapolations when determining the quantization steps. Proposed method has better performances than the MPEG-4 frame-based bitrate control algorithm by evaluating with the actually encoded bits per frame, encoded PSNR's, and frame skips.

• Journal of KIISE
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• v.41 no.11
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• pp.974-981
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• 2014

These days, use of multimedia streaming service and demand of QoS (Quality of Service) improvement have been increased because of development of network. QoS of streaming service is influenced by a jitter, delay, throughput, and loss rate. For guaranteeing these factors which are influencing QoS, the role of transport layer is very important. But existing TCP which is a typical transport layer protocol increases the size of congestion window slowly and decreases the size of a congestion window drastically. These TCP characteristic have a problem which cannot guarantee the QoS of UHD multimedia streaming service. In this paper, we propose a router buffer based congestion control method for improving the QoS of UHD streaming services. Our proposed scheme applies congestion window growth rate differentially according to a degree of congestion for preventing an excess of available bandwidth and maintaining a high bandwidth occupied. Also, our proposed scheme can control the size of congestion window according to a change of delay. After comparing with other congestion control protocols in the jitter, throughput, and loss rate, we found that our proposed scheme can offer a service which is suitable for the UDH streaming service.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.162-170
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• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.835-844
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• 2004

Recently, the location-based service for moving customers is becoming one of the most important service in mobile communication area. For moving object applications, there are lots of update operations and such update loads are concentrated on some particular area unevenly. Range queries, whose range can be window or circular, are the most essential query types in LBS. We need to distinguish completely contained cells from partially contained cells in those range queries. Also, it is necessary to consider temporal dimension to filter out qualifying objects correctly. In this paper, we adopt two-level index structures with fixed grid file structures in the second level, which are designed to minimize update operations. We propose a spatial ceil filtering method using VP filtering and a combined spatio-temporal filtering method using time gone concepts. Some experimental results are shown for various window queries and circular queries with different filtering combinations to show the performance tradeoffs of the proposed methods.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.327-330
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• 2002

인터넷 환경의 데이터 베이스에 접속하여 물품을 적은 경비를 들여 신속하고 효율적으로 원하는 장소에 때를 맞추어보낼 수 있도록 함으로써 가치를 창출하는 경제 활동 즉, 자재 및 제품의 포장·수송·하역·보관·통신 등의 활동을 시스템적으로 개발 함으로서 보다 효율적이고, 신속한 물류의 흐름을 파악할 수 있다. 이 논문에서는 대단위 트랜잭션 처리가 가능한 컴포넌트 기반의 고 가용성 물류시스템의 구축을 목표로 구성하였다. 이 시스템은 윈도우 DNA 기반의 컴포넌트 기반구조를 중점으로 구현했으며 TP 모니터위주의 설계를 중점으로 하였다. 따라서 한가지만의 정보가 아닌 전체적인 포탈 시스템으로 전반적인 정보의 활용을 할 수 있는 시스템이다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.34-37
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• 2004

인터넷을 기반으로 하는 금융서비스를 제공함에 있어 보안성에 대한 필요성이 최근 들어 매우 중요시되어져가고 있으며, 이 분야에 대한 많은 연구가 진행되어 오고 있다. 국내 대부분의 은행 기관에서는 인터넷 뱅킹 서비스를 지원하고 있으나, 그 서비스 이용에 있어 윈도우 사용자에 국한되어 있는 실정이다. 본 논문에서는 리눅스 기반에서 보다 더 안전한 인터넷 뱅킹 서비스를 지원하기 위한 PKI 기반 기술을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.377-379
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• 2003

최근 개인용 컴퓨터 사용자들의 인터넷 사용이 빈번해지면서. 개인용 컴퓨터 시스템에 대한 다양한 네트워크 공격의 피해가 늘고 있다. 이에 개인용 방화벽이 개발되었으나 현재 대부분이 윈도우 시스템에 기반하고 있다. 하지만 최근 리눅스 시스템이 개인용 컴퓨터의 운영체제로 많이 사용되고 있고, 피해 사례가 상당히 많음에도 불구하고 아직까지 리눅스 기반 개인용 방화벽은 전무한 실정이다. 본 논문에서는 기존의 리눅스 기반 네트워크 방화벽에서 제공하는 기능들 외에 동적 정책 설정 기능과 악의적인 네트워크 프로그램 실행의 제어 기능들을 추가적으로 포함하는 리눅스 기반 개인용 방화벽을 설계하고 구현하였다.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.253-260
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• 2015

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.345-354
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• 2005

Subsequence matching is one of the most important operations in the field of data mining. The existing subsequence matching algorithms use only one index, and their performance gets worse as the difference between the length of a query sequence and the site of windows, which are subsequences of a same length extracted from data sequences to construct the index, increases. In this paper, we propose a new subsequence matching method based on index interpolation to overcome such a problem. An index interpolation method constructs two or more indexes, and performs search ing by selecting the most appropriate index among them according to the given query sequence length. In this paper, we first examine the performance trend with the difference between the query sequence length and the window size through preliminary experiments, and formulate a search cost model that reflects the distribution of query sequence lengths in the view point of the physical database design. Next, we propose a new subsequence matching method based on the index interpolation to improve search performance. We also present an algorithm based on the search cost formula mentioned above to construct optimal indexes to get better search performance. Finally, we verify the superiority of the proposed method through a series of experiments using real and synthesized data sets.