• 전자공학회논문지
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• 제52권12호
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• pp.77-86
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• 2015

LIDAR를 이용해서 얻은 3차원 점군 데이터는 작은 물체를 추출하기에는 오차의 영향이 크기 때문에 작은 밸브를 자동으로 추출하는데 많은 어려움이 있다. 본 논문에서는 이러한 잡음이 있는 3차원 점군 데이터 사이에서 밸브의 위치 및 방향(Pose)의 정보를 얻는 방법을 제안한다. Pose를 얻기 위해서 밸브가 원환체 모양의 손잡이, 원통 모양의 Rib, 평면 모양의 중심축 평면인 기본 도형으로 이루어진 모델이라고 가정한다. 그리고 밸브의 중심 좌표에 대한 추가적인 입력을 받아서 밸브의 Pose를 추출한다. 중심점을 기준으로 거리에 따른 히스토그램을 생성하고, 히스토그램의 값에 따라 손잡이, Rib, 중심축 평면의 파라미터를 통계적인 방법으로 추출하여 최종 밸브의 Pose를 추출한다. 추출된 밸브의 Pose를 이용하여 3차원 점군 데이터에 밸브의 모형을 각 모양으로 복원한다.

• Biotechnology and Bioprocess Engineering:BBE
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• 제9권6호
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• pp.454-458
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• 2004

A study of the POSE (predispersed solvent extraction) for succinic acid by colloidalliq­uid aphrons was conducted. The organic phase contaning TOA (tri-n-octylamine) and 1-octanol permits a selective extraction of succinic acid from its aqueous solution. There was no difference of the extractability of POSE and that of conventional mixer-settler type extraction. Taking into account the no mechanical mixing in POSE, it was concluded that the POSE process is more adaptive than the conventional mixer-settler type extraction process. From mass transfer analysis at the various concentration of TOA in counter-current continuous operation, the concentration of TOA had no influence on the mass transfer coefficient. The loading values in continuous POSE were almost same as those in batch operation.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1745-1753
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• 2004

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1244-1248
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• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2000-2003
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• 2003

This paper presents a three-dimensional (3D) head pose estimation algorithm for robust face recognition. Given a 3D input image, we automatically extract several important 3D facial feature points based on the facial geometry. To estimate 3D head pose accurately, we propose an Error Compensated-SVD (EC-SVD) algorithm. We estimate the initial 3D head pose of an input image using Singular Value Decomposition (SVD) method, and then perform a Pose refinement procedure in the normalized face space to compensate for the error for each axis. Experimental results show that the proposed method is capable of estimating pose accurately, therefore suitable for 3D face recognition.

• 한국의류산업학회지
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• 제4권6호
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• pp.544-549
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• 2002

By the movable thermal manikin developed by China Dong Hua university, the laws of clothing thermal insulation influenced by dressing poses are studied. It is found that $I_a$ on nude thermal manikin has no relation to testing pose as a whole (notable level is 5%), while the change of testing pose influences $I_a$ value on parts of body obviously. The testing result $I_{cle}$ on clothed thermal manikin has relation to testing pose. The $I_{cle}$ value of the whole body in seated pose decreases 20 percent compared with that in standing pose (notable level is 1%). In view of heat transmission theory, the reasons are pointed out based on the knowledge of heat transmission.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.455-456
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• 2006

This paper presents an automatic pose-normalized 3D face data acquisition method using 2D and 3D information. We propose an automatic pose-normalized 3D face acquisition method that accomplishes 3D face modeling and 3D face pose-normalization at once. The proposed method uses 2D information with AAM (Active Appearance Model) and 3D information with 3D normal vector. The 3D face modeling system consists of 2 cameras and 1 projector. In order to verify proposed pose-normalized 3D modeling method, we made an experiment for 2.5D face recognition. The experimental result shows that proposed method is robust against pose variation.

• 전기학회논문지
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• 제59권2호
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• pp.429-435
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• 2010

Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We earned out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권8호
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• pp.3011-3024
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• 2021

Pose estimation of the sensor is important issue in many applications such as robotics, navigation, tracking, and Augmented Reality. This paper proposes visual-inertial integration system appropriate for dynamically moving condition of the sensor. The orientation estimated from Inertial Measurement Unit (IMU) sensor is used to calculate the essential matrix based on the intrinsic parameters of the camera. Using the epipolar geometry, the outliers of the feature point matching are eliminated in the image sequences. The pose of the sensor can be obtained from the feature point matching. The use of IMU sensor can help initially eliminate erroneous point matches in the image of dynamic scene. After the outliers are removed from the feature points, these selected feature points matching relations are used to calculate the precise fundamental matrix. Finally, with the feature point matching relation, the pose of the sensor is estimated. The proposed procedure was implemented and tested, comparing with the existing methods. Experimental results have shown the effectiveness of the technique proposed in this paper.

• 자동차안전학회지
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• 제14권3호
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• pp.71-76
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• 2022

In autonomous driving cars, 3D pose estimation can be one of the effective methods to enhance safety control for OOP (Out of Position) passengers. There have been many studies on human pose estimation using a camera. Previous methods, however, have limitations in automotive applications. Due to unexplainable failures, CNN methods are unreliable, and other methods perform poorly. This paper proposes robust real-time multi-human 3D pose estimation architecture in vehicle using monocular RGB camera. Using particle filter, our approach integrates CNN 2D/3D pose measurements with available information in vehicle. Computer simulations were performed to confirm the accuracy and robustness of the proposed algorithm.