• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.326-331
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• 2019

In the real world, there are both dynamic objects and static objects, but an autonomous vehicle or mobile robot cannot distinguish between them, even though a human can distinguish them easily. It is important to distinguish static objects from dynamic objects clearly to perform autonomous driving successfully and stably for an autonomous vehicle or mobile robot. To do this, various sensor systems can be used, like cameras and LiDAR. Stereo camera images are used often for autonomous driving. The stereo camera images can be used in object recognition areas like object segmentation, classification, and tracking, as well as navigation areas like 3D world reconstruction. This study suggests a method to distinguish static/dynamic objects using stereo vision for an online autonomous vehicle and mobile robot. The method was applied to a 3D world map reconstructed from stereo vision for navigation and had 99.81% accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.119-125
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• 2008

This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and Cascade SVM to enhance the fusion model with DBAM and ABAM process.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.97-104
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• 2012

Natural User Interface(NUI) technologies introduce new trends in using devices such as computer and any other electronic devices. In this paper, an augmented reality on a mobile device is implemented by using face detection, recognition and motion tracking. The face detection is obtained by using Viola-Jones algorithm from the images of the front camera. The Eigenface algorithm is employed for face recognition and face motion tracking. The augmented reality is implemented by overlapping the rear camera image and GPS, accelerator sensors' data with the 3D graphic object which is correspond with the recognized face. The algorithms and methods are limited by the mobile device specification such as processing ability and main memory capacity.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.139-147
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• 2022

The goal of this study is to develop a mobile application so that a person who is new to chemistry can easily acquire the knowledge necessary for chemical structure learning using image tracking technology. The point of this study is to provide a new chemical structure learning experience by recognizing a two-dimensional picture, augmenting the chemical structure into a three-dimensional object, showing it on the user's screen, and using a service that simultaneously provides related information in multiple fields. characteristic. Login API and real-time database technology were used for safe and real-time data management, and an application was developed using image tracking technology for image recognition and 3D object augmentation service. In the future, we plan to use the chemical structure data library to efficiently load and output data.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.1-10
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• 2019

Object-based image matching algorithms have been widely used in the image processing and computer vision fields. A variety of applications based on image matching algorithms have been recently developed for object recognition, 3D modeling, video tracking, and biomedical informatics. One prominent example of image matching features is the Scale Invariant Feature Transform (SIFT) scheme. However many applications using the SIFT algorithm have implemented based on stand-alone basis, not client-server architecture. In this paper, We initially implemented based on client-server structure by using SIFT algorithms to identify and match objects in biomedical images to provide useful information to the user based on the recently released Mobile platform. The major methodological contribution of this work is leveraging the convenient user interface and ubiquitous Internet connection on Mobile device for interactive delineation, segmentation, representation, matching and retrieval of biomedical images. With these technologies, our paper showcased examples of performing reliable image matching from different views of an object in the applications of semantic image search for biomedical informatics.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1463-1471
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• 2012

In this study, a 3-axis camera system design is proposed for application to an existing 2-axis surveillance robot. A camera-image-based target-tracking algorithm for this robot has also been proposed. The algorithm has been validated using a virtual simulation. In the algorithm, the heading direction vector of the camera system in the mobile surveillance robot is obtained by the position error between the center of the view finder and the center of the object in the camera image. By using the heading direction vector of the camera system, the desired pan and tilt angles for target-tracking and the desired roll angle for the stabilization of the camera image are obtained through inverse kinematics. The algorithm has been validated using a virtual simulation model based on MATLAB and ADAMS by checking the corresponding movement of the robot to the target motion and the virtual image error of the view finder.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.35-41
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• 2012

This paper proposes a moving object detection algorithm for active camera system that can be applied to mobile robot and intelligent surveillance system. Most of moving object detection algorithms based on a stationary camera system. These algorithms used fixed surveillance system that does not consider the motion of the background or robot tracking system that track pre-learned object. Unlike the stationary camera system, the active camera system has a problem that is difficult to extract the moving object due to the error occurred by the movement of camera. In order to overcome this problem, the motion of the camera was compensated by using SURF and Pseudo Perspective model, and then the moving object is extracted efficiently using stochastic Label Cluster transport model. This method is possible to detect moving object because that minimizes effect of the background movement. Our approach proves robust and effective in terms of moving object detection in active camera system.

• International Journal of Contents
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• v.10 no.1
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• pp.1-11
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• 2014

In the current generation of smart mobile devices, object tracking is one of the most important research topics for computer vision. Because human face tracking can be widely used for many applications, collecting a dataset of face videos is necessary for evaluating the performance of a tracker and for comparing different approaches. Unfortunately, the well-known benchmark datasets of face videos are not sufficiently diverse. As a result, it is difficult to compare the accuracy between different tracking algorithms in various conditions, namely illumination, background complexity, and subject movement. In this paper, we propose a new dataset that includes 91 face video clips that were recorded in different conditions. We also provide a semi-automatic ground-truth generation tool that can easily be used to evaluate the performance of face tracking systems. This tool helps to maintain the consistency of the definitions for the ground-truth in each frame. The resulting video data set is used to evaluate well-known approaches and test their efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.948-954
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• 1999

In this paper, we propose a cooperative motion planning algorithm for two tightly-coupled mobile robots. Specifically, the considered cooperative work is that two mobile robots should transfer a long rigid object along a predefined path. To resolve the problem, we introduce a master-slave concept for two obile robots having the same structure. According to the velocity of the master robot and the positions of two robots on the path, the velocity of the slave robot is determined. The slave normally tracks the master's motion, but in case that the velocity of the slave exceeds the velocity limit, the roles of the robots should be interchanged. The effectiveness of the proposed algorithm is proved by computer simulations.

• Smart Media Journal
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• v.7 no.2
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• pp.15-22
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• 2018

Pupil tracking technologies can be used as an efficient information provider means that provides convenience to the user by connecting with a smart device. In this paper, we measure the distance of user gaze point using the pupil tracking device which produced by Pupil-labs, also shows the experimental result with analyzing accuracy and precision. Based on that the pupil gaze point location which tracked by pupil tracking device is compared with object target in terms of error. Since the mobile pupil tracking device is also one kind of camera, we have to perform the calibration before using the device. Not only generally used 2-dimensional calibration, but also 3-dimensional calibration method is explained. To get the improved accuracy of 2-dimensional calibration result, the 3-dimensional calibration set an imaginary plane and executes the calibration in various 3-dimensional spaces. To show the efficiency of 3-dimensional calibration, we analyze the experimental result. It also introduces various using methods and information that can be obtained through the device.