• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1574-1580
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• 2020

Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.247-257
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• 2021

This paper proposes a new technique for calibrating a multi-view RGB-D camera using a 3D (dimensional) skeleton. In order to calibrate a multi-view camera, consistent feature points are required. In addition, it is necessary to acquire accurate feature points in order to obtain a high-accuracy calibration result. We use the human skeleton as a feature point to calibrate a multi-view camera. The human skeleton can be easily obtained using state-of-the-art pose estimation algorithms. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D skeleton obtained through the posture estimation algorithm as a feature point. Since the human body information captured by the multi-view camera may be incomplete, the skeleton predicted based on the image information acquired through it may be incomplete. After efficiently integrating a large number of incomplete skeletons into one skeleton, multi-view cameras can be calibrated by using the integrated skeleton to obtain a camera transformation matrix. In order to increase the accuracy of the calibration, multiple skeletons are used for optimization through temporal iterations. We demonstrate through experiments that a multi-view camera can be calibrated using a large number of incomplete skeletons.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.177-179
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• 2021

This paper proposes a method of estimating location of a target point at which an interactive robot gazes in an indoor space. RGB images are extracted from low-cost web-cams, user head pose is obtained from the face detection (Openface) module, and geometric configurations are applied to estimate the user's gaze direction in the 3D space. The coordinates of the target point at which the user stares are finally measured through the correlation between the estimated gaze direction and the plane on the table plane.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.499-506
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• 2006

This Paper describes a fast and stable camera pose estimation method for real-time augmented reality systems. From the feature tracking results of a marker on a single frame, we estimate the camera rotation matrix and the translation vector. For the camera pose estimation, we use the shape factorization method based on the scaled orthographic Projection model. In the scaled orthographic factorization method, all feature points of an object are assumed roughly at the same distance from the camera, which means the selected reference point and the object shape affect the accuracy of the estimation. This paper proposes a flexible and stable selection method for the reference point. Based on the proposed method, we implemented a video augmentation system that inserts virtual 3D objects into the input video frames. Experimental results showed that the proposed camera pose estimation method is fast and robust relative to the previous methods and it is applicable to various augmented reality applications.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.195-199
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• 2008

We present an approach to detect real plane for line base recognition and pose estimation Given 3D line segments, we set up reference plane for each line pair and measure the normal distance from the end point to the reference plane. And then, normal distances are measured between remains of line endpoints and reference plane to decide whether these lines are coplanar with respect to the reference plane. After we conduct this coplanarity test, we initiate visibility test using z-buffer value to prune out ambiguous planes from reference planes. We applied this algorithm to real images, and the results are found useful for evidence fusion and probabilistic verification to assist the line based recognition as well as 3D pose estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1528-1534
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• 2019

Recently, due to the popularization of 3D depth cameras, new researches and opportunities have been made in research conducted on RGB images, but estimation of human hand pose is still classified as one of the difficult topics. In this paper, we propose a robust estimation method of human hand pose from various input 3D depth images using a learning algorithm. The proposed approach first generates a skeleton-based hand model and then aligns the generated hand model with three-dimensional point cloud data. Then, using a random forest-based learning algorithm, the hand pose is strongly estimated from the aligned hand model. Experimental results in this paper show that the proposed hierarchical approach makes robust and fast estimation of human hand posture from input depth images captured in various indoor and outdoor environments.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.50-56
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• 2004

The directional ability of an automobile has an influence on driver directly, and hence it must be given most priority. Alignment factors of automobile such as the camber, caster and toe directly affect the directional ability of a vehicle. The above mentioned factors are determined by the pose of interlinks in the assembly of an automobile front chassis module. Measuring the position of center point of ball joints in the front lower arm is very difficult. A method to determine this position is suggested in this paper. Pose estimation for front chassis module and dimensional evaluation to find the rotational characteristics of front lower arm were developed based on fundamental geometric techniques. To interpret the inspection data obtained for front chassis module, 3-D best fit method is needed. The best fit method determines the relationship between the nominal design coordinate system and the corresponding feature coordinate system. The least squares method based on singular value decomposition is used in this paper.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.300-309
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• 2016

This paper proposes a method for motion estimation of consecutive cameras using 3-D straight lines. The motion estimation algorithm uses two non-parallel 3-D line correspondences to quickly establish an initial guess for the relative pose of adjacent frames, which requires less correspondences than that of current approaches requiring three correspondences when using 3-D points or 3-D planes. The estimated motion is further refined by a nonlinear optimization technique with inlier correspondences for higher accuracy. Since there is no dominant line representation in 3-D space, we simulate two line representations, which can be thought as mainly adopted methods in the field, and verify one as the best choice from the simulation results. We also propose a simple but effective 3-D line fitting algorithm considering the fact that the variance arises in the projective directions thus can be reduced to 2-D fitting problem. We provide experimental results of the proposed motion estimation system comparing with state-of-the-art algorithms using an open benchmark dataset.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.47-48
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• 2021

본 논문에서는 대용량의 3D 데이터 시퀀스의 압축을 진행한다. 3D 데이터 시퀀스의 각 프레임에서 Pose Estimation을 통해 3D Skeleton을 추출한 뒤, 포인트 클라우드를 skeleton에 묶는 리깅 과정을 거치고, 다음 프레임과 같은 자세로 deformation을 진행한다. 다음 프레임과 같은 자세로 변형된 포인트 클라우드와 실제 다음 프레임의 포인트 클라우드를 비교하여, 두 데이터에 모두 있는 점, 실제 다음 프레임에만 있는 점, deformation한 데이터에만 있는 점으로 분류한다. 두 데이터에 모두 있는 점을 제외하고 나머지 두 분류의 점들을 저장함으로써 3D 시퀀스 데이터를 압축할 수 있다.