• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.6
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• pp.605-612
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• 2015

Three dimensional (3D) position determination and motion recognition using a 3D depth sensor camera are applied to a developed penguin-shaped robot, and its validity and closeness are investigated. The robot is equipped with an Asus Xtion Pro Live as a 3D depth camera, and a sound module. Using the skeleton information from the motion recognition data extracted from the camera, the robot is controlled so as to follow the typical three mode-reactions formed by the operator's gestures. In this study, the extraction of skeleton joint information using the 3D depth camera is introduced, and the tracking performance of the operator's motions is explained.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.406-410
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• 2005

A depth measurement system that consists of a single camera, a laser light source and a rotating mirror is investigated. The camera and the light source are fixed, facing the rotating mirror. The laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The camera detects the laser light location on object surfaces through the same mirror. The scan over the area to be measured is done by mirror rotation. Advantages are 1) the image of the light stripe remains sharp while that of the background becomes blurred because of the mirror rotation and 2) the only rotating part of this system is the mirror but the mirror angle is not involved in depth computation. This minimizes the imprecision caused by a possible inaccurate angle measurement. The detail arrangement and experimental results are reported.

• Physical Therapy Rehabilitation Science
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• v.13 no.1
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• pp.106-112
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• 2024

Objective: This study aimed to evaluate the validity of a depth camera-based system for monitoring physical function, assessing its feasibility for accurately monitoring activities of daily living. Design: A cross-sectional study. Methods: Twenty-three participants were enlisted to perform fifteen activities of daily living within a living laboratory designed to simulate a home environment. Activities were monitored using a depth camera system capable of classifying actions into standing, sitting, and lying down, with a conventional video camera employed for activity recording. The duration of each activity, as measured by the system, was compared to direct observations made by a physical therapist which were analyzed using a motion analysis software. The association between these two measurement approaches was assessed through correlation analysis, coefficient of determination, intraclass correlation coefficient (ICC), and Bland-Altman plots. Results: Our findings indicated that standing activities exhibited the highest correlation (r=0.847) between the system measurements and physical therapist observations, followed by sitting (r=0.817) and lying down (r=0.734), which demonstrated lower correlations. However, the ICC and Bland-Altman plots revealed notable variances between the two measurement methods, particularly for activities involving lying down. Conclusions: In this study, the depth camera-based physical monitoring system showed promise feasibility in distinguishing standing, sitting, and lying down activities at home environments. However, the current study also underlined some necessities of enhancements in capturing lying down activities.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.31-37
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• 2018

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1504-1507
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• 2007

Depth data for is very important data for 3D display. Disparity and depth data makes users to feel 3D effect. We used stereo camera to measure depth and made fast algorithm to get in real time. This vision system can be substituted for expensive laser system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.181.5-181
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• 2001

This paper presents the method of 3D reconstruction of the depth information from the endoscopic stereo scopic images. After camera modeling to find camera parameters, we performed feature-point based stereo matching to find depth information. Acquired some depth information is finally 3D reconstructed using the NURBS(Non Uniform Rational B-Spline) algorithm. The final result image is helpful for the understanding of depth information visually.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1320-1327
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• 2009

In stereoscopy using stereo camera system, the representative factors inducing stereoscopic depth distortions have been reported such as the inter camera distance, the convergence angle and the depth resolution. The image distance is just known to us as a factor related in optical system. In a point of view of depth distortion, it will be a factor inducing a stereoscopic depth distortion. In this paper, we focused on the proof of our opinion that the image distance is one of the weighted factors inducing depth distortion under orthostereoscopic condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.637-644
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• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.1-7
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• 2012

This paper presents a method for 3D image construction using the hybrid (color and depth) camera system, in which the drawbacks of each camera can be compensated for. Prior to an image generation, intrinsic parameters and extrinsic parameters of each camera are extracted through experiments. The geometry between two cameras is established with theses parameters so as to match the color and depth images. After the preprocessing step, the relation between depth information and distance is derived experimentally as a simple linear function, and 3D image is constructed by coordinate transformations of the matched images. The present scheme has been realized using the Microsoft hybrid camera system named Kinect, and experimental results of 3D image and the distance measurements are given to evaluate the method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.229-230
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• 2022

본 논문에서는 CNN Object Detection과 더불어 3D 카메라 기반 디지털 좌표 인식 기술을 제안한다. 이 기술은 3D Depth Camera인 Intel 사의 Realsense D455를 이용해 대상을 감지하고 분류하며 대상의 위치를 파악한다. 또한 이 기술은 기존의 Depth Camera 내장 거리와는 다르게 좌표를 인식하여 좌표간의 거리까지 계산이 가능하다. 또한 Tensorflow SSD 구조와의 메모리 공유를 통해 시스템의 자원 낭비를 줄이며, 속도를 높이는 멀티쓰레드를 탑재했다. 본 기술을 통해 좌표간의 거리를 계산함으로써 스포츠, 심리, 놀이, 산업 등 다양한 환경에서 활용할 수 있다.