• Journal of Multimedia Information System
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• v.4 no.4
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• pp.285-288
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• 2017

In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.774-779
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• 2016

The conventional methods using color signal for tracking the movement of the object require a lot of calculation and the performance is not accurate. In this paper, we propose a method to effectively track the moving objects using the depth information from a depth camera. First, it separates the background and the objects based on the depth difference in the depth of the screen. When an object is moved, the depth value of the object becomes blurred because of the phenomenon of Motion Blur. In order to solve the Motion Blur, we observe the changes in the characteristics of the object (the area of the object, the border length, the roundness, the actual size) by its velocity. The proposed algorithm was implemented in the simulation that was applied directly to the tracking of a golf ball. We can see that the estimated value of the proposed method is accurate enough to be very close to the actual measurement.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.1-6
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• 2004

Motion Information in a video clip often plays an important role in characterizing the content of the clip. A number of methods have been developed to analyze and retrieve video contents using motion information. However, most of these methods focused more on the analysis of direction or trajectory of motion but less on the analysis of the movement of an object. In this paper, we introduce the shape variation descriptor for describing shape variation caused by object movement along time, and propose a method to describe and retrieve the shape variation of the object using shape variation map. The experimental results shows that the proposed method performed much better than the previous method by l1％ and is very effective for describing the shape variation which is applicable to semantic retrieval applications.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.35-42
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• 2013

Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.514-517
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• 2003

Oxygen supply is one of the most basic things in human body. Breathing is controlled by the lungs' stationary function and the respiratory center which is in the mesulla oblongata. Nothing but, the external breathing that air movement between the lungs and atmosphere and the internal breathing that cellular air movement between the hemoglobin and a single cell. The adult's number of times of the respirations is about 15∼20 per 1 minute, but it depends ages, exercise, temperature, disease, etc. The important thing in detecting the respiration is that doing it in object person's resting time. Detecting the respiration have to be done without attracting any attention of object person. In present using method is detecting the pulse with catching an object person's wrist and observing the object person's movement. In this paper, we propose the mobile respiration detection diagnostic system using ultrasound sensing method that does not be influenced by the inertia error and the pressure error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.856-862
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• 2002

The object-oriented(00) distributed real-time(RT) programming movement started in1990's and is growing rapidly at this turn of the century. Distributed real-time simulation is a field in its infancy but it is bounded to receive steadily growing recognition for its importance and wide applicability. The scheme is called the distributed time-triggered simulation scheme which is conceptually simple and easy to use but widely applicable. A new generation object oriented(00) RT programming scheme is called the time-triggered message triggered object(TMO)programming scheme and it is used to make specific illustrations of the issues. The TMO structuring scheme is a general-style components structuring scheme and supports design of all types of component including hard real time objects and non real time objects within one general structure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.69-72
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• 2011

In this paper, we propose a method to track the movement of camera from the video sequences. This method is useful for video analysis and can be applied as pre-processing step in some application such as video stabilizer and marker-less augmented reality. First, we extract the features in each frame using corner point detection. The features in current frame are then compared with the features in the adjacent frames to calculate the optical flow which represents the relative movement of the camera. The optical flow is then analyzed to obtain camera movement parameter. The final step is camera movement estimation and correction to increase the accuracy. The method performance is verified by generating a 3D map of camera movement and embedding 3D object to the video. The demonstrated examples in this paper show that this method has a high accuracy and rarely produce any jitter.

• Journal of the HCI Society of Korea
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• v.5 no.2
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• pp.15-24
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• 2010

A Digilog Book is an augmented reality (AR) based next generation publication supporting both sentimental analog emotions and digitized multi-sensory feedbacks by combining a conventional printed book and digital contents. As a Digilog Book authoring software, ARtalet provides an intuitive authoring environment through 3D user interface in AR environment. In this paper, we suggest ARtalet authoring environment based trajectory editing method to generate and manipulate a movement path of an augmented 3D object on the Digilog Book. Specifically, the translation points of the 3D manipulation prop is examined to determine that the point is a proper control point of a trajectory. Then the interpolation using splines is conducted to reconstruct the trajectory with smoothed form. The dynamic score based selection method is also exploited to effectively select small and dense control points of the trajectory. In an experimental evaluation our method took the same time and generated a similar amount of errors as the usual approach, but reduced the number of control points needed by over 90%. The reduced number of control points can properly reconstruct a movement path and drastically decrease the number of control point selections required for movement path modification. For control manipulation, the task completion time was reduced and there was less hand movement needed than with conventional method. Our method can be applicable to drawing or curve editing method in immersive In-Situ AR based education, game, design, animation, simulation application domains.

• Journal of the Ergonomics Society of Korea
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• v.16 no.1
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• pp.1-13
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• 1997

In this research, and investigation of the eye movement was performend when the objects perceived by the eye are three-dimensional objects, two dimensional images, and trhee dimensional images. This investigation was done by observing the EQG(Electro- oculogram) waves which were obtained from experiments. The observed waves were categorized into several groups. Differences among the waves were analyzed for each object and image perceived by the eye. In order to obtain waves for analysis, two kinds of experiments were performen. In each experiment, the saccadic eye movement and the smooth pursuit eye movement were considered as an independent variables. Waves obtained from the experiments were categorized into 4 types (Wave-1, Wave-2, Wave-3, Wave-4) depending on their characteristics. Unlike the other images, three dimensional image was revealed as a key factor for the active movement of the eye. Futhermore, a unique eye movement was observed in the case of three dimensional images where the focus of the eye was achieved through three steps.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.168-185
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• 1996

In this research, an investigation of the eye movement was performed when the objects perceived by the eye are three-dimensional objects, two dimensional images, and three dimensional image. This investigation was done by observing the EOG(Electro-oculogram) waves which were achived from experiments. The observed waves were categorized into several groups. Differences among the waves were analyzed for each object and image perceived by the eye. In order to obtain waves to be analyzed, two kinds of experiments were performed. In each experiments, the saccadic eye movement and the smooth pusuit eye movement was considered as an independent variable individually. Waves obtained from the experiments were categorized into 4 types(Wave1, Wave2, Wave3, Wave4) depending on their characteristics. Unlike from the other images, three dimensional image was revealed as a key factor for the active movement of the eye. Futhermore, a unique eye movement was observed in the case of three dimensional image where the focus of the eye was achived in three steps.