• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.616-624
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• 2002

An extraction method of central objects in the color images is proposed, in this paper. A central object is defined as a comparatively consist of the central object in the image. First of all. an input image and its decreased resolution images are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent regions are included by a same region in the decreased resolution image. Then core object regions and core background regions are selected from the inner region and the outer region respectively. Core object regions are the representative regions for the object and are selected by using the information about the information about the region size and location. Each inner regions is classified into foreground or background regions by comparing values of a color histogram intersection of the inner region against the core object region and the core background regions. The core object region and foreground regions consist of the central object in the image.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.39-44
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• 2022

The purpose of this study is to extract the type, location, and absolute size of an object in an image using a deep learning algorithm, predict the relative distance between objects, and use this to detect contact between objects. To analyze the size ratio of objects, YOLO, a CNN-based object detection algorithm, is used. Through the YOLO algorithm, the absolute size and position of an object are extracted in the form of coordinates. The extraction result extracts the ratio between the size in the image and the actual size from the standard object-size list having the same object name and size stored in advance, and predicts the relative distance between the camera and the object in the image. Based on the predicted value, it detects whether the objects are in contact.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.823-828
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• 2006

In this paper, we addressed the computer interfacing method using the physical component in our living environment especially, a structure that has plane shape. A beamforming is the one of method for spatial location estimate, when appling it to the structure that has plane shapes, it estimates the event of impings location. and this impings event of specific location can be used for input unit of computing system. we expected, this result will contribute to human-computer interfacing research in home network environment.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.125-134
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• 2006

The problem of establishing the servo system to reach the desired location keeping all features in the field of view and following a straight line is considered. In addition, robustness of camera calibration parameters is considered in this paper. The proposed approach is based on switching from position-based visual servoing (PBVS) to image-based visual servoing (IBVS) and allows the camera path to follow a straight line. To achieve the objective, a pose estimation method is required; the camera's target pose is estimated from the obtained images without the knowledge of the object. A switched control law moves the camera equipped to a robot end-effector near the desired location following a straight line in Cartesian space and then positions it to the desired pose with robustness to camera calibration error. Finally simulation results show the feasibility of the proposed visual servoing technique.

• Speech Sciences
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• v.10 no.3
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• pp.187-209
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• 2003

In this paper, we examine if the lexical information of the verb and its noun object affects the pitch accent patterns of the verb phrase focus. Three types of verb-object combinations with different semantic weights are discussed: when the verbs have optional direct objects, when the objects have the greater semantic weight relative to verbs, and when the verbs and the objects have equal semantic weight. Argument-structure-based works note that the pitch accent location in a focused phrase is closely related to the argument structure and contextual information. For example, it has been argued that contextually new noun objects receive accent while given noun objects don't. Contrary to nouns, verbs can be accented or not in verb phrase focus regardless of whether they are given information or new information (Selkirk 1984, 1992). However, the production experiment in this paper shows that the accenting of verbs is not fully optional, but influenced by the lexical semantic information of the verbs. The accenting of noun objects with given information is possible and the deaccenting of new noun objects also occurs depending on the lexical information of the noun objects. The results demonstrate that in addition to argument structure and information by means of context sentences, the lexical semantic information of words influences the pitch accent location in focused phrase.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.22-29
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• 2022

Recently, with the development of mobile computing power, mobile-based VR and AR services are being developed. Due to network performance and computing power constraints, VR and AR services using large-capacity 3D content have limitations. A study on an efficient 3D content load method for a mobile device is required. The conventional method downloads all 3D content used for AR services at the same time. In this paper, we propose an active 3D content load according to the user's track. The proposed method is a partitioned 3D object load. Edge servers were installed for each area and connected through the MESH network. Partitioned load the required 3D object in the area referring to the user's location. The location is identified through the edge server information of the connected AP. The performance of the proposed method and the conventional method was compared. As a result of the comparison, the proposed method showed a stable Mobile AR Service. The results of this study, it is expected to contribute to the activation of edge server-based AR mobile services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1518-1524
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• 2014

In this paper, a new method of spatial query, which is called Surround Search (SuSe) is suggested. This method makes it possible to search for the closest spatial object of interest to the user from a query point. SuSe is differentiated from the existing spatial object query schemes, because it locates the closest spatial object of interest around the query point. While SuSe searches the surroundings, the spatial object is saved on an R-tree, and MINDIST, the distance between the query location and objects, is measured by considering an angle that the existing spatial object query methods have not previously considered. The angle between targeted-search objects is found from a query point that is hidden behind another object in order to distinguish hidden objects from them. The distinct feature of this proposed scheme is that it can search the faraway or hidden objects, in contrast to the existing method. SuSe is able to search for spatial objects more precisely, and users can be confident that this scheme will have superior performance to its predecessor.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.63-70
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• 2010

We are able to find many materials of pendulum dynamic/analysis using linearized model. Usually, analysis of pendulum is to calculate for velocity, period and angle by linearized model on Newton's law. In this paper, analyzed periodical movement of pendulum using nonlinear load model. That is, analyzed load value according to location of moving pendulum at real time. And for the shake of maneuver for pendulum's location, found load control value and compared result of linearized mode with nonlinear model. The result makes know that it is possibility of nonlinear load control model to apply to various model of movement object including flight object, pendulum and etc.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.105-116
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• 2001

Moving objects are spatiotemporal data that location and shape of spatial objects are changed continuously over time. If spatiotemporal moving objects are managed by conventional database system, moving objects management systems have two problems as follows. First, update for location information changed over time is occurred frequently. Second, past and future information of moving objects are not provided by system because only current state of objects is stored in the system. Therefore, in this paper, we propose a spatiotemporal moving objects management system which is able to not only manage historical information of moving objects without frequent update, but also provide all location information about past, current, and near future. In the proposed system, information of moving objects are divided into location information for representing location and motion information for representing moving habits. Especially, we propose the method which can search location information all objects by use of changing process algorithms with minimum history information. Finally, we applied the proposed method to battlefield analysis system, as the result of experiment, we knew that past, current, and near future location information for moving objects are managed by relational database and GIS system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.1
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• pp.76-83
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• 2012

In a real-time indoor place recognition system using image features detection, specific markers included in input image should be detected exactly and quickly. However because the same markers in image are shown up differently depending to movement, direction and angle of camera, it is required a method to solve such problems. This paper proposes a technique to extract the features of object without regard to change of the object scale. To support real-time operation, it adopts SURF(Speeded up Robust Features) which enables fast feature detection. Another feature of this system is the user mark designation which makes possible for user to designate marks from input image for location detection in advance. Unlike to use hardware marks, the feature above has an advantage that the designated marks can be used without any manipulation to recognize location in input image.