• Title/Summary/Keyword: Fitting and Matching Objects

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Human Assisted Fitting and Matching Primitive Objects to Sparse Point Clouds for Rapid Workspace Modeling in Construction Automation (-건설현장에서의 시공 자동화를 위한 Laser Sensor기반의 Workspace Modeling 방법에 관한 연구-)

  • KWON SOON-WOOK
    • Korean Journal of Construction Engineering and Management
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    • v.5 no.5 s.21
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    • pp.151-162
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    • 2004
  • Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.

Developing Expert System for Recovering the Original Form of Ancient Relics Based on Computer Graphics and Image Processing (컴퓨터 그래픽스 및 영상처리를 이용한 문화 원형 복원 전문가시스템 개발)

  • Moon, Ho-Seok;Sohn, Myung-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.6 s.44
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    • pp.269-277
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    • 2006
  • We propose a new expert system for recovering the broken fragments of relics into an original form using computer graphics and image processing. This paper presents a system with an application to tombstones objects of flat plane with letters carved in for assembling the fragments by placing their respective fragments in the right position. The matching process contains three sub-processes: aligning the front and letters of an object, identifying the matching directions, and determining the detailed matching positions. We apply least squares fitting, vector inner product, and geometric and RGB errors to the matching process. It turned out that 2-D translations via fragments-alignment enable us to save the computational load significantly. Based on experimental results from the damaged cultural fragments, the performance of the proposed method is illustrated.

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3D Object Recognition and Accurate Pose Calculation Using a Neural Network (인공신경망을 이용한 삼차원 물체의 인식과 정확한 자세계산)

  • Park, Gang
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.23 no.11 s.170
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    • pp.1929-1939
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    • 1999
  • This paper presents a neural network approach, which was named PRONET, to 3D object recognition and pose calculation. 3D objects are represented using a set of centroidal profile patterns that describe the boundary of the 2D views taken from evenly distributed view points. PRONET consists of the training stage and the execution stage. In the training stage, a three-layer feed-forward neural network is trained with the centroidal profile patterns using an error back-propagation method. In the execution stage, by matching a centroidal profile pattern of the given image with the best fitting centroidal profile pattern using the neural network, the identity and approximate orientation of the real object, such as a workpiece in arbitrary pose, are obtained. In the matching procedure, line-to-line correspondence between image features and 3D CAD features are also obtained. An iterative model posing method then calculates the more exact pose of the object based on initial orientation and correspondence.

Survey on Detection and Recognition of Road Marking

  • Vokhidov, Husan;Hong, Hyung Gil;Hoang, Toan Minh;Kang, JinKyu;Park, Kang Ryoung;Cho, Hyeong Oh
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.1408-1410
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    • 2015
  • Information about the painted road markings and other painted road objects play an important part in keeping safety of drivers. Some researchers have presented research approaches and dealt with road markings detection. In this paper, we present comprehensive survey of these techniques, and review some of them like a machine learning method, template matching method for road markings detection and classification, method of detection and classification of road markings using curve-based prototype fitting, signed edge signature method.

Surveillance Video Retrieval based on Object Motion Trajectory (물체의 움직임 궤적에 기반한 감시 비디오의 검색)

  • 정영기;이규원;호요성
    • Journal of Broadcast Engineering
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    • v.5 no.1
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    • pp.41-49
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    • 2000
  • In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

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