• 한국통신학회논문지
• /
• 제25권6A호
• /
• pp.870-884
• /
• 2000

OLAT(On-Line Analytical Processing) 기법에서 스타 또는 눈송이(snowflake) 스키마에 기반한 ROLAP(Relational OLAP)은 성능 저하라는 문제가 있고, 다차원 데이터베이스에 기반한 MOLAP(Multidinmensional OLAP)은 데이터 크기 증가에 따른 공간 문제가 있다. 본 논문에서는 기존의 OLAP 시스템이 이러한 문제점을 해결하기 위해서 객체-관계 DBMS에 기반한 다차원 데이터 모델을 제안하였다. 객체-관계 DBMS가 가지는 확장성 특징을 사용하여 다차원 데이터 모델에 최적화된 다차원 개념과 함수를 정의할 수 있었다. 또한 객체-관계 DBMS의 객체간 계승 기능을 통하여 상위 테이블을 계승받는 요약 다차원 데이터 큐브의 다차원 데이터 모델을 설계하였다. 이와 같은 OLAP을 위한 데이터 타입과 함수가 정의되면, 새로운 객체-관계 DBMS 엔진과 같이 내장된 기능처럼 동작되어 성능향상이 가능하다. 또한 객체 관계 DBMS의 하나인 Informix Universal Server와 클라이언트 개발 도구를 이용하여 제안된 다차원 데이터 모델을 구현하였다.

• 한국멀티미디어학회논문지
• /
• 제19권8호
• /
• pp.1345-1360
• /
• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권12호
• /
• pp.5507-5528
• /
• 2016

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2688-2703
• /
• 2022

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

• 로봇학회논문지
• /
• 제4권3호
• /
• pp.185-191
• /
• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• 한국정밀공학회지
• /
• 제15권2호
• /
• pp.107-113
• /
• 1998

Grinding operation has difficulty in satisfying the qualitative knowledge based on the skilful expert as well as the quantitative data for all user. Design of grinding database based on the frame-based model is more effective method for utilizing the empirical and qualitative knowledge. In this paper. basic strategy to develop the grinding database by taking frame-based model, which is strongly dependent upon experience and intuition, is described. Grinding database based on the frame based model for designing the interaction and inference among the slots is accomplised by the object-oriented paradigm system.

• 인터넷정보학회논문지
• /
• 제15권1호
• /
• pp.171-178
• /
• 2014

본 논문에서는 실시간으로 가상현실의 증강객체에 외부의 힘이 작용할 때 증강된 가상 객체의 동적 모델링 방법을 제시하였다. 가상객체의 자연스러운 움직임을 시뮬레이션 하기 위하여 AR 객체에 적용되는 외부의 힘의 변화에 대하여 Newton의 운동법칙을 적용하여 객체의 움직임을 설명하는 식을 생성하였다. 동적 모델링 과정에서 증강된 객체와 햅틱 장비간의 실질적 상호작용이 발생하며 이때 외부의 힘이 가상객체에 전달된다. 증강된 객체의 고유특성은 강체 혹은 탄성체의 성질을 갖는 모델이다. 강체의 동적 모델링에서는 선형 모멘텀과 각속도 모멘텀을 모두 고려하여 증강된 객체와 햅틱 스틱이 충돌할 때 수행하였다. 비강체의 동적 모델링에 있어서는 탄성체의 변형 모델은 내외의 힘과 제한요소에 자연적으로 반응하기 때문에 물리기반 시뮬레이션 방법을 적용하였다. 증강된 탄성체는 햅틱 인터페이스를 통해 사용자에 의하여 발생하는 힘의 특성과 모델의 고유 특성에 따라 자연스럽게 변형된다. 변형 물체의 모델링을 위하여 Newton의 제 2 운동법칙이라 불리는 질량-스프링 연결 시스템을 적용하였다. 실험을 통하여 증강된 강체와 비강체의 성질을 지닌 가상 객체에 햅틱 장비에 의한 햅틱 상호작용이 발생 할 때 객체의 변환을 자연스럽게 가시화 할 수 있었다.

• 한국융합학회논문지
• /
• 제10권11호
• /
• pp.79-84
• /
• 2019

영상을 기반으로 하는 기술들의 지속적인 발전으로 다양한 분야에서 활용되고 있고, 카메라를 통하여 획득한 영상의 객체를 분석하고 판별하는 비전 시스템의 기술 수요가 급속하게 증가하고 있다. 비전 시스템의 핵심 기술인 영상처리는 반도체 생산 분야의 불량 검사, 타이어 표면의 숫자 및 심볼과 같은 객체 인식 검사 등에 사용되고 있고, 자동차 번호판 인식 등의 연구가 계속하여 이루어지고 있는 실정으로, 객체를 신속, 정확하게 인식할 필요가 있다. 본 논문에서는 곡면과 같은 곳에 마킹되어 있는 숫자나 심볼과 같이 기울어진 객체를 인식하기 위하여 입력된 영상 이미지의 객체 기울기에 대한 각도 값을 확인하여 객체의 회전 정렬을 통한 인식 모델을 제안한다. 제안 모델은 컨투어 알고리즘을 기반으로 객체 영역을 추출하고, 객체의 각도를 산출한 후, 회전 정렬된 이미지에 대한 객체 인식을 진행할 수 있는 모델이다. 향후 연구에서는 기계학습을 통한 탬플릿 매칭 연구가 필요하다.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.893-903
• /
• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
• /
• pp.160-169
• /
• 1995

This study presents an application of object-oriented methodology for structural dcsign process. A prototype system of integrated a structural design system is developed by introducing a structural analysis object model(SAOM) and structural design object model(SDOM). The SAOM module. which is modeled as a part of structural member, performs structural analysis using FEM approach and the SDOM module checks structural members based on Korea steel design standard. Above mentionedmodelsareabstraclencapsulatibleandreusable.