• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.450-450
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• 2006

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1330-1339
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• 2022

3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.90-96
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• 2020

Recently, a method that applies laser scanning (LS) that acquires vegetation information such as the vegetation habitat area and the size of vegetation in a point cloud format has been proposed. When LS is used to investigate the physical shape of vegetation, it has the advantage of more accurate and rapid information acquisition. However, to examine uncertainties that may arise during measurement or post-processing, the process of adjusting the data by the actual data is necessary. Therefore, in this study, the physical structure of stems, branches, and leaves of woody vegetation in an artificially formed river channel was manually investigated. The obtained results then compared with the information acquired using the three-dimensional terrestrial laser scanning (3D TLS) method, which repeatedly scanned the target vegetation in various directions to obtain relevant information with improved precision. The analysis demonstrated a negligible difference between the measurements for the diameters of vegetation and the length of stems; however, in the case of branch length measurement, a relatively more significant difference was observed. It is because the implementation of point cloud information limits the precise differentiation between branches and leaves in the canopy area.

• Journal of the Korea Computer Graphics Society
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• v.16 no.2
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• pp.29-37
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• 2010

In this paper, we present a novel deformation method for alleviating the asymmetry of a scanned 3D face considering facial features. To handle detailed areas of the face, we developed a new local 3D shape descriptor based on facial features and surface curvatures. Our shape descriptor can improve the accuracy when deforming a 3D face toward a symmetric configuration, because it provides accurate point pairing with respect to the plane of symmetry. In addition, we use point-based representation over all stages of symmetrization, which makes it much easier to support discrete processes. Finally, we performed a statistical analysis to assess subjects' preference for the symmetrized faces by our approach.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.139-140
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• 2008

지상라이다는 고정도의 3차원 영상을 제공하고 레이저 빔을 현장이나 대상물에 발사하여 짧은 시간에 수백만점의 3차원좌표를 기록할 수 있는 최신 측량장비로서 다양한 응용분야에서 두각을 나타내고 있다. 본 연구에서는 지상라이다를 이용한 건축물의 3자윈 자동 윤곽선 추출을 다룬다. 지상라이다는 건축물의 3차원 윤곽선을 신속하게 추출할 수 있지만 지상기반 시스템이므로 여러 가지 장애물 때문에 건국물의 하단부에서는 추출이 쉽지 않다. 기존 항공라이다를 이용한 알고리즘에서는 사진의 색상차나 모폴로지 특성에 의존하여 범위를 제한하고, 이를 기반으로 윤곽선을 추출하였다. 하지만 지상라이다의 경우 항공라이다에 비해 분해능이 월등히 높다. 또한, 지상라이다는 지상에서 측정하기 때문에 항공라이다에서 어려운 건축물의 측면이나 정면도 윤곽선 추출이 가능하기 때문에 본 논문에서는 사진을 이용하지 않고 전처리를 하지 않은 데이터를 직접 이용하여 건물의 정면 윤곽선을 추출하는 것을 제안한다. 건물의 크기와 데이터 수 즉, 라이다로 측정한 포인트 수를 고려한 효율적인 Decimation방법을 제안하고 또한, Decimation된 데이터이서 지역적으로 제일 큰 값과 작은 값을 찾아낸다. 그 중 많이 벗어난 점을 편차를 이용하여 제거한다. 이렇게 찾아낸 건축물의 외곽점들을 이어 만든 윤곽선을 최종적으로 보간하여 좀 더 현실과 가까운 윤곽선 추출 방법을 제안한다.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.705-714
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• 2019

Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 34.54 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.159-160
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• 2022

본 논문에서는 최근 주요시설의 경계에 주로 사용되기 시작한 라이다 센서를 여러대 사용할때 보다 효율적으로 사용하기 위해서 통합된 3차원 좌표계에서 시각화하는 방법에 대해 설명한다. 주로 카메라 기반 CCTV의 경우 정확성은 높지만 시야각(Field of View)이 좁기 때문에 레이더(RADAR)센서와 같은 센서와 함께 혼용되는 경우가 많다. 레이더 센서의 데이터는 넓은 범위에 대한 감지를 할 수 있지만 노이즈가 많고 물체의 형상을 정확하게 측정하기 힘들다. 라이다(LiDAR) 센서는 레이져를 이용하여 멀고 넓은 범위를 정교하게 측정할 수 있다. 이러한 라이다 센서는 정교한 만큼 처리해야할 데이터의 양이 많으며, 다수의 센서를 이용하더라도 하나의 화면에서 처리하기 힘들다는 단점이 있다. 제안하는 논문은 여러개의 라이다 센서에서 측정한 데이터를 실시간에 하나의 좌표계로 통일하여 하나의 영상을 보일 수 있도록 통합 뷰잉 환경을 제공한다.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.507-520
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• 2011

Conventional radar altimeter system measured directly the distance between the satellite and the ocean surface and frequently used by aircraft for approach and landing. The radar altimeter is good at flat surface like sea whereas it is difficult to determine precise three dimensional ground coordinates because the ground surface, unlike ocean, is very indented. To overcome this drawback of the radar altimeter, we have developed and validated the interferometric radar altimeter signal processing which is combined with new synthetic aperture and interferometric signal processing algorithm to extract precise three-dimensional ground coordinates. The proposed algorithm can accurately measure the three dimensional ground coordinates using three antennas. In a set of 70 simulations, the averages of errors in x, y and z directions were approximately -0.40 m, -0.02 m and 4.22 m, respectively and the RMSEs were about 3.40 m, 0.30 m and 6.20 m, respectively. The overall results represent that the proposed algorithm is effective for accurate three dimensional ground positioning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.388-393
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• 2019

As a fiiting method to data with outliers, RANSAC(RANdom SAmple Consensus) based algorithm is widely used in fitting of line, circle, ellipse, etc. CUDA is currently most widely used GPU with massive parallel processing capability. This paper proposes an efficient CUDA implementation of multiple planes fitting using RANSAC with 3d points data, of which one set of 3d points is used for one plane fitting. The performance of the proposed algorithm is demonstrated compared with CPU implementation using both artificially generated data and real 3d heights data of a PCB. The speed-up of the algorithm over CPU seems to be higher in data with lower inlier ratio, more planes to fit, and more points per plane fitting. This method can be easily applied to a wide variety of other fitting applications.