• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.2
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• pp.16-22
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• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.85-88
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• 2009

In this paper, we propose a DRF-based object detection method using the object adaptive patch in the satellite imagery. It is a Discriminative Random Fields (DRF) based work, so the detection is done by labeling to the possible patches in the image. For the feature information of each patch, we use the multi-scale and object adaptive patch and its texton histogram, instead of using the single scale and fixed grid patch. So, we can include contextual layout of texture information around the object. To make object adaptive patch, we use "superpixel lattice" scheme. As a result, each group of labeled patches represents the object or object's presence region. In the experiment, we compare the detection result with a fixed grid scheme and shows our result is more close to the object shape.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2284-2290
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• 2015

The use of human actions as context for object class recognition is quite effective in enhancing the recognition performance despite the large variation in the appearance of objects. We propose an efficient method that integrates human action information into object class recognition using a Bayesian appraoch based on a simple probabilistic graph model. The experiment shows that by using human actions ac context information we can improve the performance of the object calss recognition from 8% to 28%.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.19-24
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• 2008

This paper presents a physically-based haptic rendering algorithm for a deformable object based on visual information about the intervention between a tool and a real object in a remote place. The physically-based model of a deformable object is created from the mechanical properties of the object and the captured image obtained with a CCD camera. When a slave system exerts manipulation tasks on a deformable object, the reaction force for haptic rendering is computed using boundary element method. Snakes algorithm is used to obtain the geometry information of a deformable object. The proposed haptic rendering algorithm can provide haptic feedback to a user without using a force transducer in a teleoperation system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.410-412
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• 2021

This paper proposes an improved pedestrian detection method using object and background histograms. Objects detected through the HOG & SVM algorithm are detected in a square shape. Inside the square area, the background and the object area are mixed. If only the area of the object excluding the background is detected, various object-related information may be easily obtained. The size of the detected rectangle is readjusted using an xy-axis projection algorithm to fit the size of the object. And then, the improved object is detected by dividing the background and the object based on the histogram of the object in the readjusted square. The average values of precision and recall, which are reliability evaluations comparing the detected object with the original object, are 97.9% and 90%, respectively.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.147-154
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• 2003

Microarray information system is a complex system to manage, analyze and interpretate microarray gene expression data. Establishment of well-defined development process is very essential for understanding the complexity and organization of the system. We performed object-oriented analysis using Unified Modeling Language (UML) in specifying, visualizing and documenting microarray information system. The object-oriented analysis consists of three major steps: (i) use case modeling to describe various functionalities from the user's perspective (ii) dynamic modeling to illustrate behavioral aspects of the system (iii) object modeling to represent structural aspects of the system. As a result of our modeling activities we provide the UML diagrams showing various views of the microarray information system. We believe that the object-oriented analysis ensures effective documentations and communication of information system requirements. Another useful feature of object-oriented technique is structural continuity to standard microarray data model MAGE-OM (Microarray Gene Expression Object Model). The proposed modeling e(forts can be applicable for integration of biomedical information system.

• Journal of KIBIM
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• v.13 no.2
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• pp.37-46
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• 2023

The main objective of this study is to propose a method to enhance building safety using the Industry Foundation Classes (IFC) schema in Building Information Modeling (BIM). To achieve this goal, a fire object relationship diagram is created by using the Model View Definition (MVD) and Property Set (Pset) methodology, as well as the Work Breakdown Structure (WBS) based object relationship analysis. The proposed method illustrates how to represent objects and tasks related to fire prevention and human safety during a building fire, including variables that are relevant to these aspects. Furthermore, the proposed method offers the advantage of considering both the IFC object hierarchy and the project work hierarchy when creating new objects, thereby expanding the attribute information for fire safety and maintenance. However, upon confirmation via an IFC viewer after development, a problem with the accuracy of mapping between attributes and objects arises due to the issue of proxy representation of related object information and newly added object information in standard IFC. Therefore, in future research, a mapping method for fire safety objects will be developed to ensure accurate representation, and the scope of utilization of the fire safety object diagram will be expanded. Furthermore, efforts will be made to enhance the accuracy of object and task representation. This research is expected to contribute significantly to the technological development of building safety and fire facility design in the future.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.192-198
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• 2020

The 3D point cloud is a key technology of object detection for virtual reality and augmented reality. In order to apply various areas of object detection, it is necessary to obtain 3D information and even color information more easily. In general, to generate a 3D point cloud, it is acquired using an expensive scanner device. However, 3D and characteristic information such as RGB and depth can be easily obtained in a mobile device. GNN (Graph Neural Network) can be used for object detection based on these characteristics. In this paper, we have generated RGB and RGBD by detecting basic information and characteristic information from the KITTI dataset, which is often used in 3D point cloud object detection. We have generated RGB-GNN with i-GNN, which is the most widely used LiDAR characteristic information, and color information characteristics that can be obtained from mobile devices. We compared and analyzed object detection accuracy using RGBD-GNN, which characterizes color and depth information.

• Management & Information Systems Review
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• v.1
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• pp.229-268
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• 1997

Recently, object-oriented concepts and technology are on the leading edge of programming language and database systems research, and their usefulness in those contexts has been successfully demonstrated. The adoption of object-oriented concept to the design of model bases has several benefits. From the perspectives of object-oriented approach, models in a model base are viewed as object which encapsulate their states and behaviors. This paper focuses on the design of an object-oriented model base that handles various resources of DSS(data, knowledge, models, solvers) in a unified fashion. For the design of a model base, we adopted Object Modeling Techniques(OMT). An object model of OMT can be used for the conceptual design of an overall model base schema. The object model of OMT provides several advantages over the conventional approaches in model base design. The main advantage are model reuse, hierarchical model construction, model sharing, meta-modeling, and unified model object management.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.5-7
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• 1996

We propose a triangulation method based on stereo vision angles. We setup stereo vision systems which extract the depth information to a moving object by detecting a moving object using difference image method and obtaining the depth information by the triangulation method based on stereo vision angles. The feature point of a moving object is used the geometrical center of the moving object, and the proposed vision system has the accuracy of 0.2mm in the range of 400mm.