• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.344-345
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• 2011

본 논문에서는 모니터 상단의 일반 웹 카메라를 이용하여 사용자의 올바른 자세와 올바르지 않은 자세를 추정하고 사용자가 스스로 교정을 유도할 수 있는 새로운 개념의 어플리케이션을 소개한다. 교정의자나 허리 보호대 등의 도구가 없이 카메라 하나로 사용자가 자기 자세에 대한 인식을 할 수 있도록 제안하는 시스템이다. 정면 시점에서 바라보는 사용자의 자세는 얼굴과 어깨의 특징으로 판단하고, 초기화한 올바른 자세와 비교하여 사용자에게 경고 알림을 해주는 과정으로 진행된다. 주기적으로 자세를 확인하는 시스템을 통하여 사용자가 컴퓨터를 하는 자세에 대해 자각시킴으로써 올바른 자세를 가지도록 유도할 수 있음을 확인하였다.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.130-131
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• 2003

쌍안경이나 망원경과 같은 장비를 통해 사물을 관측할 때, 사용자는 손떨림에 의한 상의 흔들림을 쉽게 경험한다. 이러한 상의 흔들림은 관측 장비의 배율이 클수록 두드러지기 때문에 흔들림을 줄이는 장치 없이 높은 배율의 상을 얻는 것은 한계를 가지게 된다. 그러므로 일반적으로 8배 이상의 배율을 가지는 관측 장비가 목적에 맞게 이용되기 위해서는 손떨림과 같은 높은 진동수와 낮은 진폭을 가지는 진동에 대해 상을 안정화시키기 위한 장치를 필요로 한다. (중략)

• Journal of Korean Ophthalmic Optics Society
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• v.10 no.4
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• pp.381-389
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• 2005

To Analysis of Correlation of Visual Function Findings, by assessing convergence and accommodation, 92 selected objects without any ocular diseases by apperatuses of visual chart(Shinnippon CT30) and phoropter(Shinnippon VT10) at a shan distance(40 cm) were tested upon MEM retinoscopy(Welch Allyn, USA), BELL retinoscopy(Welch Allyn, USA), binocular accommodative facility (${\pm}2.00$ D nipper, Bernell Co., USA), vergence facility(prism nipper, Bernell Co., USA). The results showed as follows. MEM retinoscopy(accommodative lag) showed the same result of a right eye and left eye. Bell retinoscopy(accommodative lag) showed higher correlations between right and left eye than MEM. The lower accommodative lag meant the higher accommodative facility. The binocular accommodative facility(polaroid) was higher than binocular accommodative facility(red-green). Correlations of accommodative facility between right and left eye were higher, and as the higher monocular accommodative facility also meant the higher binocular accommodative facility, monocular and binocular accommodative facilities were relative to vergence facility, These findings can be used as a clinical guide by curing patients' visual function.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.34-40
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• 2018

This paper presents a new method to estimate the pose of a moving object accurately using a monocular camera and a low-end GPS+IMU sensor system. For this goal, we adopted a deep neural network for the semantic segmentation of input images and compared the results with a semantic map of a neighborhood. In this map matching, we use weight tables to deal with label inconsistency effectively. Signals from a low-end GPS+IMU sensor system are used to limit search spaces and minimize the proposed function. For the evaluation, we added noise to the signals from a high-end GPS-IMU system. The results show that the pose can be recovered from the noisy signals. We also show that the proposed method is effective in handling non-open-sky situations.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.441-447
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• 2006

See-through HMD type 3D display can provide an advantage of us seeing virtual 3D data used stereoscopic display simultaneously with real object(MR-Mixed Reality). But, when user sees stereoscopic display for a long time, not only eye fatigue phenomenon happens but also de-focus phenomenon of data happens by fixed focal point of virtual data. Dissatisfaction of focus adjustment of eye can be considered as the important reason of this phenomenon. In this paper, We proposed an application of multi-focus in see-through HMD as a solution of this problem. As a result, we confirmed that the focus adjustment coincide between the object of real world and the virtual data by multi-focus in monocular condition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.24-34
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• 2007

Application systems that easy to access a information have been developed by IT growth and a human life variation. In this paper, we propose a application system to register a 3D footwear model using a monocular camera. In General, a human motion analysis research to body movement. However, this system research a new method to use a foot movement. This paper present a system process and show experiment results. For projection to 2D foot plane from 3D shoe model data, we construct processes that a foot tracking, a projection expression and pose estimation process. This system divide from a 2D image analysis and a 3D pose estimation. First, for a foot tracking, we propose a method that find fixing point by a foot characteristic, and propose a geometric expression to relate 2D coordinate and 3D coordinate to use a monocular camera without a camera calibration. We make a application system, and measure distance error. Then, we confirmed a registration very well.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.65-72
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• 2009

This paper presents a vision based localization method for indoor mobile robots using the types of vertices from a monocular image. In the images captured from a camera of a robot, the types of vertices are determined by searching vertical edges and their branch edges with a geometric constraints. For obtaining correspondence between the comers of a 2-D map and the vertex of images, the type of vertices and geometrical constraints induced from a geometric analysis. The vertices are matched with the comers by a heuristic method using the type and position of the vertices and the comers. With the matched pairs, nonlinear equations derived from the perspective and rigid transformations are produced. The pose of the robot is computed by solving the equations using a least-squares optimization technique. Experimental results show that the proposed localization method is effective and applicable to the localization of indoor environments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.267-274
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• 2009

Recently, UAVs (Unmanned Aerial Vehicles) are expected much as the Unmanned Systems for various missions. These missions are often based on the Vision System. Especially, missions such as surveillance and pursuit have a process which is carried on through the transmitted vision data from the UAV. In case of small UAVs, monocular vision is often used to consider weights and expenses. Research of missions performance using the monocular vision is continued but, actually, ground and target model have difference in distance from the UAV. So, 3D distance measurement is still incorrect. In this study, Mean-Shift Algorithm, Optical Flow and Subspace Method are posed to estimate the relative depth. Mean-Shift Algorithm is used for target tracking and determining Region of Interest (ROI). Optical Flow includes image motion information using pixel intensity. After that, Subspace Method computes the translation and rotation of image and estimates the relative depth. Finally, we present the results of this study using images obtained from the UAV experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.12
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• pp.1445-1452
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• 2013

In this paper, we propose a real-time algorithm for estimating the relative position of a person with respect to a robot (camera) using a monocular camera. The algorithm detects the head and shoulder regions of a person using HOG (Histogram of Oriented Gradient) feature vectors and an SVM (Support Vector Machine) classifier. The size and location of the detected area are used for calculating the relative distance and angle between the person and the camera on a robot. To increase the speed of the algorithm, we use a GPU and NVIDIA's CUDA library; the resulting algorithm speed is ~ 15 Hz. The accuracy of the algorithm is compared with the output of a SICK laser scanner.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.