Real-time Camera Tracking for Augmented Reality Applications: Efficient Line and Patch Feature Characertization and Management

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9783639107395: Real-time Camera Tracking for Augmented Reality Applications: Efficient Line and Patch Feature Characertization and Management
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One of the key problems of augmented reality is the tracking of the camera position and viewing direction in real-time. Current vision-based systems mostly rely on the detection and tracking of fiducial markers. Some markerless approaches exist, which are based on 3D line models or calibrated reference images. These methods require a high manual preprocessing work step, which is not applicable for the efficient development and design of industrial AR applications. The problem of the preprocessing overload is addressed by the development of vision-based tracking algorithms, which require a minimal workload of the preparation of reference data. A novel method for the automatic view-dependent generation of line models in real-time is presented. The tracking system only needs a polygonal model of a reference object, which is often available from the industrial construction process. Point-based methods which rely on optical flow-based template tracking are developed for the camera pose estimation in partially known scenarios. The robustness and real-time capability is improved with a statistical approach for a feature management system which is based on machine learning techniques.

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Harald Wuest received his diploma degree in computer engineering from the University of Mannheim in 2004. Since 2004 he has been a research fellow at the Fraunhofer Institute for Computer Graphics in Darmstadt. In 2008 he received his PhD in computer science from the University of Darmstadt, Germany.

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Book Description Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | Efficient Line and Patch Feature Characertization and Management | One of the key problems of augmented reality is thetracking of the camera position and viewing directionin real-time. Current vision-based systems mostlyrely on the detection and tracking of fiducialmarkers. Some markerless approaches exist, which arebased on 3D line models or calibrated referenceimages. These methods require a high manualpreprocessing work step, which is not applicable forthe efficient development and design of industrial ARapplications.The problem of the preprocessing overload isaddressed by the development of vision-based trackingalgorithms, which require a minimal workload of thepreparation of reference data.A novel method for the automatic view-dependentgeneration of line models in real-time is presented.The tracking system only needs a polygonal model of areference object, which is often available from theindustrial construction process.Point-based methods which rely on optical flow-basedtemplate tracking are developed for the camera poseestimation in partially known scenarios. Therobustness and real-time capability is improved witha statistical approach for a feature managementsystem which is based on machine learning techniques. | Format: Paperback | Language/Sprache: english | 208 gr | 144 pp. Seller Inventory # K9783639107395

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Book Description VDM Verlag Jan 2009, 2009. Taschenbuch. Condition: Neu. Neuware - One of the key problems of augmented reality is the 144 pp. Englisch. Seller Inventory # 9783639107395

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Book Description VDM Verlag, 2009. Paperback. Condition: New. Language: English . Brand New Book. One of the key problems of augmented reality is the tracking of the camera position and viewing direction in real-time. Current vision-based systems mostly rely on the detection and tracking of fiducial markers. Some markerless approaches exist, which are based on 3D line models or calibrated reference images. These methods require a high manual preprocessing work step, which is not applicable for the efficient development and design of industrial AR applications. The problem of the preprocessing overload is addressed by the development of vision-based tracking algorithms, which require a minimal workload of the preparation of reference data. A novel method for the automatic view-dependent generation of line models in real-time is presented. The tracking system only needs a polygonal model of a reference object, which is often available from the industrial construction process. Point-based methods which rely on optical flow-based template tracking are developed for the camera pose estimation in partially known scenarios. The robustness and real-time capability is improved with a statistical approach for a feature management system which is based on machine learning techniques. Seller Inventory # KNV9783639107395

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