• Title/Summary/Keyword: 실패-재경로 선택

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An Enhanced Rerouting Function using the Failure Information in a VANET Unicasting Routing (VANET 유니캐스팅 라우팅에서 실패 정보를 이용한 경로 재탐색 기능의 강화)

  • Lee, Won Yeoul;Lee, Wan-Jik
    • Journal of Korea Multimedia Society
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    • v.17 no.2
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    • pp.191-199
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    • 2014
  • The unicasting routing technology of VANET is very important for user convenience. Unicasting packets must be forwarded to the appropriate path in order to arrive to the destination. However, there are so many problems because the vehicle nodes have limited information related to the routing decision. In particular, packet delivery failure will be occurred by selecting the path already failed again. We call this problem as 'Failed Path Re-Selection Problem'. In this paper, we propose an enhanced rerouting function of VANET Routing. The proposed rerouting function uses the failed path information when rerouting function executed. For this rerouting function, failed path information will be stored in the packet whenever the routing fail occurred. By the comparison with the performance of legacy VANET routing function, the superiority of the proposed method can be seen.

Online Multi-view Range Image Registration using Geometric and Photometric Feature Tracking (3차원 기하정보 및 특징점 추적을 이용한 다시점 거리영상의 온라인 정합)

  • Baek, Jae-Won;Moon, Jae-Kyoung;Park, Soon-Yong
    • The KIPS Transactions:PartB
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    • v.14B no.7
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    • pp.493-502
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    • 2007
  • An on-line registration technique is presented to register multi-view range images for the 3D reconstruction of real objects. Using a range camera, we first acquire range images and photometric images continuously. In the range images, we divide object and background regions using a predefined threshold value. For the coarse registration of the range images, the centroid of the images are used. After refining the registration of range images using a projection-based technique, we use a modified KLT(Kanade-Lucas-Tomasi) tracker to match photometric features in the object images. Using the modified KLT tracker, we can track image features fast and accurately. If a range image fails to register, we acquire new range images and try to register them continuously until the registration process resumes. After enough range images are registered, they are integrated into a 3D model in offline step. Experimental results and error analysis show that the proposed method can be used to reconstruct 3D model very fast and accurately.