• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.448-450
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• 2012

본 논문에서는 Kinect 방식의 RGB-D 영상센서를 사용하여, 깊이(Depth) 영상으로부터 3차원 객체의 기하정보를 표현하는 표면 정규 벡터(Surface Normal Vector)를 추출하고, 그 결과를 영상화하는 방법을 제안하며, 제안된 방법으로 생성된 영상으로부터 깊이 영상의 특징점 및 특징 기술자를 추출하여 3차원 객체 인식 성능을 향상시키는 방법을 제안한다. 또한 생성된 RGB-D 특징 기술자들을 객체 단위로 구분 가능한 코드북(CodeBook) 학습을 통한 인식방법을 제안하여 객체의 인식 성능을 높이는 방법을 제안한다. 제안하는 RGB-D 기반의 특징 추출 및 학습 방법은 텍스쳐 유무, 카메라 회전 및 이동 변화 등의 환경변화에 강건함을 실험적으로 증명하였으며, 이 방법은 Kinect 방식의 RGB-D 영상을 사용하는 3차원 객체/공간 인식 및 추적, 혹은 이를 응용하는 증강현실 시스템에 적용하여 사용될 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.309-314
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• 2014

To generate a complete 3D model from depth images of multiple RGB-D cameras, it is necessary to find 3D transformations between RGB-D cameras. This paper proposes a convenient view calibration technique using a spherical object. Conventional view calibration methods use either planar checkerboards or 3D objects with coded-pattern. In these conventional methods, detection and matching of pattern features and codes takes a significant time. In this paper, we propose a convenient view calibration method using both 3D depth and 2D texture images of a spherical object simultaneously. First, while moving the spherical object freely in the modeling space, depth and texture images of the object are acquired from all RGB-D camera simultaneously. Then, the external parameters of each RGB-D camera is calibrated so that the coordinates of the sphere center coincide in the world coordinate system.

• 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 Korea Computer Graphics Society
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• v.24 no.3
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• pp.61-71
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• 2018

When triangular meshes are generated from the point clouds in global space reconstructed through camera pose estimation against captured RGB-D streams, the quality of the resulting meshes improves as more triangles are hired. However, for 3D reconstructed models beyond some size threshold, they become to suffer from the ugly-looking artefacts due to the insufficient precision of RGB-D sensors as well as significant burdens in memory requirement and rendering cost. In this paper, for the generation of 3D models appropriate for real-time applications, we propose an effective technique that extracts high-quality textures for moderate-sized meshes from the captured colors associated with the reconstructed point sets. In particular, we show that via a simple method based on the mapping between the 3D global space resulting from the camera pose estimation and the 2D texture space, textures can be generated effectively for the 3D models reconstructed from captured RGB-D image streams.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.162-163
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• 2012

In this paper, we propose a synthesis method of elemental images from Kinect images for 3D integral imaging display. Since RGB images and depth image obtained from Kinect are not able to display 3D images in integral imaging system, we need transform the elemental images in integral imaging display. To do so, we synthesize the elemental images based on the geometric optics mapping from the depth plane images obtained from RGB image and depth image. To show the usefulness of the proposed system, we carry out the preliminary experiments using the two person object and present the experimental results.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.159-162
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• 2003

컬러 특징을 나타내는 전형적인 방법으로 RGB 컬러히스토그램이 있다. RGB 컬러 히스토그램은 RGB 컬러 공간을 일련의 bin으로 나누고 각 bin의 컬러 값에 대응하는 영상 픽셀의 빈도 수를 카운트하는 것으로 영상 검색에서 널리 쓰이고 있다. 그러나 RGB 컬러히스토그램은 영상의 미세한 장면 변화에도 크게 달라지기 때문에 검색 결과에 많은 영향을 줄 수 있다. 본 논문에서는 RGB 컬러히스토그램의 이러한 특성을 개선할 수 있는 새로운 컬러 특징 표현 방법을 제안한다. 제안하는 방법은 RGB 컬러 공간으로부터 RG, GB, BR의 세 가지 평면으로 투영시킨 2차원 투영 맵(2D Projection Maps)을 사용한다. 영상 검색에 대한 실험 결과로부터 제안한 2차원 투영 맵이 RGB 컬러히스토그램보다 더 향상된 검색 효율을 나타내었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.248-249
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• 2022

In this paper, we propose a method for classifying and tracking objects based on information of multiple users obtained using RGB-D cameras. The 3D information and color information acquired through the RGB-D camera are acquired and information about each user is stored. We propose a user classification and location tracking algorithm in the entire image by calculating the similarity between users in the current frame and the previous frame through the information on the location and appearance of each user obtained from the entire image.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.766-769
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• 2015

본 논문에서는 컬러 영상과 깊이 영상으로부터 영상 전체의 정보를 활용하는 형판 정합 방법으로 특징을 추출하여, 사물의 속성을 인식하는 시스템을 제안한다. 본 시스템은 입력 영상으로부터 더 많은 정보를 얻기 위해 컬러 영상과 깊이 영상을 함께 사용하였다. 그리고 영상의 부분적인 정보가 아닌 전체 정보를 활용하는 형판 정합 방법을 사용하여 속성 인식률을 향상 시켰다. 본 시스템의 성능을 확인하기 위해 워싱턴 대학에서 제공하는 RGB-D 데이터 집합을 이용하여 다른 특징들 및 분류기와 비교실험을 진행하였고, 본 논문에서 제안하는 시스템의 높은 성능을 확인할 수 있었다.

• Journal of Urban Science
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• v.6 no.2
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• pp.17-27
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• 2017

Recently, civil complaints have increased due to water pollution and bad smell in rivers. Therefore, attention is focused on improving the river environment. The purpose of this study is to acquire RGB and thermal infrared images using UAV for sewage outlet and to monitor the status of stream pollution and the applicability UAV based images for river embankment maintenance plan was examined. The accuracy of the 3D model was examination by SfM(Structure from Motion) based images analysis on river embankment maintenance area. Especially, The wastewater discharged from the factory near the river was detected as an thermal infrared images and the flow of wastewater was monitored. As a result of the study, we could monitor the cause and flows of wastewater pollution by detecting temperature change caused by wastewater inflow using UAV images. In addition, UAV based a high precision 3D model (DTM, Digital Topographic Map, Orthophoto Mosaic) was produced to obtain precise DSM(Digital Surface Model) and vegetation cover information for river embankment maintenance.

• Journal of IKEEE
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• v.23 no.1
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• pp.44-52
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• 2019

We implement an educational indoor autonomous mobile robot system that integrates LiDAR sensing information with RGB-D camera image information and exploits the integrated information. This system uses the existing sensing method employing a LiDAR with a small number of scan channels to acquire LiDAR sensing information. To remedy the weakness of the existing LiDAR sensing method, we propose the 3D structure recognition technique using depth images from a RGB-D camera and the deep learning based object recognition algorithm and apply the proposed technique to the system.