• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.126-133
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• 2003

This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.362-371
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• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.179-182
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• 2001

본 논문은 웹 상에서 사용자에게 동적 컨텐츠를 제공하기 위한 멀티미디어 데이터베이스 관리 시스템의 일부로써 시스템의 하부 구조 및 기본 API 를 제공하는 멀티미디어 데이터베이스 클래스를 설계하고, 이를 기반으로 사용자 요구에 대한 멀티미디어 객체를 추출하는 질의 처리 모텔을 제시한다. 멀티미디어 데이터베이스 클래스는 다양한 형태의 멀티미디어 데이터에 대한 분류 지원 및 관련 객체를 집합으로 관리하는 기능과 멀티미디어 메타데이터 생성 및 관리 기능을 제공하며, 질의 처리 모델은 이러한 멀티미디어 데이터베이스 클래스에서 관리되는 멀티미디어 객체 및 메타 객체를 효율적으로 추출한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.683-685
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• 2018

본 논문은 도로에서의 객체탐지를 위한 딥러닝(deep learning) 데이터셋을 자동으로 생성, 분류하는 시스템을 제안한다. 시스템의 작동 과정은 크게 두 가지이다. 먼저 딥러닝을 활용하여 촬영된 영상에 존재하는 객체를 검출한다. 이때, 실시간으로 하는 방법과 레코딩된 영상을 다루는 방법 두 가지가 있다. 다음으로 검출된 객체 중 예측 값(scroe)가 임계치 이상인 객체의 위치와 종류를 파일로 저장한다. 이 시스템은 차량 전방 카메라 위치에 장착된 웹캠을 이용해 영상을 취득하고 임베디드 보드인 TX2 board를 이용해 데이터 셋을 생성한다. 매트랩의 image labeler app과 비교를 통해 보다 적은 시간비용으로 데이터셋을 생성해 냄을 확인하였다.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.9-24
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• 2008

In this paper, we describes the design of software architecture supporting for healthcare context information service platform based on multi agent in home environment. In this platform, the DOGF supports the execute object and healthcare sensors and device's logical services grouping. JADE framework can support mobility in heterogeneous environment. The multi agents on platform order to support a healthcare context information service it will be able to divide. An agent collects an environment information from distributed devices. Another an agent follows mobile-device specific and it does a different service. And an agent where it manages like this. The mobile-proxy&agent is an interface part between DOGF and JADE, support data interchange or mobility pattern. For DOGF and JADE to provide healthcare context information service, we describes the design of multi agent software platform and multi agent classification by services. Finally we showed the system environments which is physical environments and prototype based on platfonn for healthcare context information services.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.315-319
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• 2007

최근 들어 전 세계적으로 자연재해가 급격하게 증가하고 있으며，국내의 경우에 있어서도 홍수，산불，지진 등과 같은 자연재해의 발생빈도， 피해규모 및 피해양상이 매우 다양해지고 었다. 따라서 이러한 다양한 피해양상에 적극적으로 대처할 수 있는 멀티 센서 피해조사 시스템의 개발 및 이를 활용한 신속하고 객관적언 피해 분석 방안이 요구되고 있다. 고해상도 위성 및 다양한 탐측센서의 개발，유비쿼터스 관련 인프라 기술의 확대，그리고 인터넷 및 데이터베이스 관련 기술의 발달은 피해지역의 공간정보의 취득 기회를 획기적으로 증가시켰으며，이러한 다양한 정보들은 멸티 센서기반의 피해정보 분석 시스템의 기반기술들로 활용이 가능하다. 본 연구는 위성영상을 이용한 풍수해 피해조사 기법에 있어서 SAR 영상의 그림자영역 제거와 기하보정 기법을 연구 개선하였으며 광학영상은 객체기반분류 기법을 적용하여 재해조사에 활용할 수 있는 방법을 제시하였다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.7
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• pp.1801-1812
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• 1998

기존의 접근 제어 메커니즘인 강제적 접근 제어와 임의적 접근 제어는 무결성을 요구하는 상용 환경의 정보 보안에는 부족하여 이의 대안으로 직무 기반 접근 제어 (RBAC:Role Based Access Control)가 주목 받고 있으며, 직무를 수행하는 사용자의 의무 분리(Separation of Duty)에 대한 연구가 최근 이루어지고 있다. RBAC에서 사용자는 직무에 배정된 접근권한(Privilege) 만을 수행하여야 하는데 상호 배타적(Mutual exclusive)특성을 갖는 직무는 표현 및 접근 권한의 직무 배정과 수행에 있어서 어려움이 있다. 본 논문에서는 RBAC의 기본 특성을 분석하여 직무에 접근권한을 부여하고 사용자를 직무에 배정하는데 따른 안전한 접근 제어를 위하여 반순서 관계를 갖는 직무의 승계 속성에 따라 직무의 계층 형태를 분류하고, 직무에 배정되는 접근권한의 표현과 관리를 용이하게 하기 위하여 객체에 부여된 객체 접근권한을 분석하여 방향성 그래프를 이용하여 기본 접근권한으로 모델링한다. 접근권한 그래프(Privilege Graph)로 표현된 기본 접근권한에 직무를 배정하면 상호 배타적 직무의 접근권한과 의무 분리의 표현 및 관리를 용이하게 할 수 있다. 이를 기반으로 의무 분리를 포함한 RBAC의 안정성 특성과 접근권한 그래프를 이용한 직무의 의무 분리를 위한 직무 관리 알고리즘을 제시한다.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.143-152
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• 2008

Nowadays, many investigators are studying various methodologies concerning event expression for semantic retrieval of video data. However, most of the parts are still using annotation based retrieval that is defined into annotation of each data and content based retrieval using low-level features. So, we propose a method of creation of the motion unit and extracting event through the unit for the more semantic retrieval than existing methods. First, we classify motions by event unit. Second, we define semantic unit about classified motion of object. For using these to event extraction, we create rules that are able to match the low-level features, from which we are able to retrieve semantic event as a unit of video shot. For the evaluation of availability, we execute an experiment of extraction of semantic event in video image and get approximately 80% precision rate.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.1-18
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• 2008

Because we can usually get many information through analyzing trajectories of moving objects on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. Also, because FNR-tree and MON-tree store the segment unit of moving objects, they can't support the trajectory of whole moving objects. In this paper, we propose an efficient trajectory index structures based on signatures on a spatial network, named SigMO-Tree. For this, we divide moving object data into spatial and temporal attributes, and design an index structure which supports not only range query but trajectory query by preserving the whole trajectory of moving objects. In addition, we divide user queries into trajectory query based on spatio-temporal area and similar-tralectory query, and propose query processing algorithms to support them. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently Finally, we show from our performance analysis that our trajectory index structure outperforms the existing index structures like FNR-Tree and MON-Tree.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.