• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.145-148
• /
• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• Korean Institute of Interior Design Journal
• /
• v.14 no.6 s.53
• /
• pp.103-111
• /
• 2005

Since the Industrial Revolution in the 18th century, the production system adopted mechanization and mass production to popularize and standardize overall society. The architectural space removed basic desire of men on the decoration to neglect historical and regional continuity of the architecture and to make uniform designs, so that human sensibility and emotion were excluded. The architectural space had arbitrary and functionalism features in accordance with such a social change to display abstract space having no personality. The limitation expanded value of the space that was not lot possession and residence but for communication with men to express object factors. However, the object factors of the space were expressed in each factor consisting of either material factors or non-material factors. This study investigated interaction of the space by both expressive interaction and potential interaction to find out key words for making frames of the investigation and to examine cases comprehensively. The purpose of the study was to recover relations between the space and men in horizontal way and mutual communication and to present possibility integrating men and space.

• Journal of Information Technology Applications and Management
• /
• v.12 no.1
• /
• pp.21-34
• /
• 2005

Object-oriented paradigm has required the fundamental changes in the developers' practice and orientation. However. the relevant literature has not still provided the solid practical methodology but created some myths regarding Use Case modeling. In this study. we attempt to 'demythologize' some key aspects of Use Case Modeling by describing what we have observed in our research and practice. In order to identify the developers' perceptions. the survey methodology was utilized. By discussing the differences between what must be done and what has been done. this study provides rich insights into the better understanding of the Use Case modeling and the successful development of object-oriented systems.

• Spatial Information Research
• /
• v.5 no.1
• /
• pp.77-88
• /
• 1997

This paper presents the design and implementaion of an object-relational geographic information system. This system has been developed on top of a commercial object-relational database management system. It provides flexible spatial data model, spatial query language, visual user interface, and efficient spatial access methods(D0T) in which traditional primary-key access methods can be applied. We report on our design choices and describe the current status of Implementation. The conceptual model of the system is based on SDTS, and is mapped to the intemal obiect-oriented data model. Kevwords : object-oriented data model, GIS, spatial data model, spatial access method.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.6
• /
• pp.319-327
• /
• 2022

Transformers are the most famous deep learning models that has achieved great success in natural language processing and also showed good performance on computer vision. In this survey, we categorized transformer-based models for computer vision, particularly object detection tasks and perform comprehensive comparative experiments to understand the characteristics of each model. Next, we evaluated the models subdivided into standard transformer, with key point attention, and adding attention with coordinates by performance comparison in terms of object detection accuracy and real-time performance. For performance comparison, we used two metrics: frame per second (FPS) and mean average precision (mAP). Finally, we confirmed the trends and relationships related to the detection and real-time performance of objects in several transformer models using various experiments.

• Proceedings of the IEEK Conference
• /
• 2001.09a
• /
• pp.337-340
• /
• 2001

본 논문에서는 MPEG가 비디오 스트림의 형상 정보를 클러스터링 하여 VO(Visual Object)의 동작을 요약하는 새로운 방법을 제안한다. 제안하는 방법은 MPEG-4 비트 스트림의 디코딩 없이 형상 정보를 근사화 한다. 그리고 사용자가 입력한 질의 VOP(Video Object Plane)와 VO의 각 VOP와의 NMHD(Normalized Mean Hausdorff Distance)를 구한 후 클러스터링을 수행하여 키 영역을 분리해낸다. 클러스터링에 의해 시간적으로 분리된 영역의 지속성을 고려하여 Rank를 매김으로써 사용자가 원하는 수의 키 VOP를 선택할 수 있게 한다. 제안하는 방법은 클러스터링을 사용함으로써 키 VOP를 선정하였으며, Rank와 질의 VOP를 사용하여 사용자와의 상호작용이 가능하다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.5
• /
• pp.479-493
• /
• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

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

• Journal of Korea Multimedia Society
• /
• v.20 no.1
• /
• pp.17-31
• /
• 2017

The aim of the paper is to present an object recognition method toward augmented reality system that utilizes existing education instruments that was designed without any consideration on image processing and recognition. The light reflection, sizes, shapes, and color range of the existing target education instruments are major hurdles to our object recognition. In addition, the real-time performance requirements on embedded devices and user experience constraints for children users are quite challenging issues to be solved for our image processing and object recognition approach. In order to meet these requirements we employed a method cascading light-weight weak classification methods that are complimentary each other to make a resultant complicated and highly accurate object classifier toward practically reasonable precision ratio. We implemented the proposed method and tested the performance by video with more than 11,700 frames of actual playing scenario. The experimental result showed 0.54% miss ratio and 1.35% false hit ratio.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.11
• /
• pp.107-116
• /
• 2012

In this paper we propose a method that can generate the risk probability map in the form of raster shape by using Markov Chain methodology applied to the object interpretation keys and quantified risk indexes. These object interpretation keys, which are primarily characteristics that can be identified by the naked eye, are set based on the objects that comprise the spatial information of a certain urban area. Each key is divided into a cell, and then is weighted by its own risk index. These keys in turn are used to generate the unified risk probability map using various levels of crime prediction probability maps. The risk probability map may vary over time and means of applying different sets of object interpretation keys. Therefore, this method can be used to prevent crimes by providing the ways of setting up the best possible police patrol beat as well as the optimal arrangement of surveillance equipments.