| Data-driven grasp synthesis using shape matching and task-based pruning. | |
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MedLine Citation:
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PMID: 17495333 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically-based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments. |
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Authors:
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Ying Li; Jiaxin L Fu; Nancy S Pollard |
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Publication Detail:
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Type: Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: IEEE transactions on visualization and computer graphics Volume: 13 ISSN: 1077-2626 ISO Abbreviation: IEEE Trans Vis Comput Graph Publication Date: 2007 Jul-Aug |
Date Detail:
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Created Date: 2007-05-14 Completed Date: 2007-07-18 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9891704 Medline TA: IEEE Trans Vis Comput Graph Country: United States |
Other Details:
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Languages: eng Pagination: 732-47 Citation Subset: IM |
Affiliation:
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School of Computer Science, Robotics Institute NSH, Carnege Mellon University, Pittsburgh, PA 15213-3890, USA. liyingus@yahoo.com |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence Computer Graphics Computer Simulation Databases, Factual Hand / anatomy & histology, physiology* Hand Strength / physiology* Humans Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Imaging, Three-Dimensional / methods* Information Storage and Retrieval / methods Models, Biological* Numerical Analysis, Computer-Assisted Pattern Recognition, Automated / methods* Task Performance and Analysis* User-Computer Interface |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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