Document Detail


Data-driven grasp synthesis using shape matching and task-based pruning.
MedLine Citation:
PMID:  17495333     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Ying Li; Jiaxin L Fu; Nancy S Pollard
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
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:
Created Date:  2007-05-14     Completed Date:  2007-07-18     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9891704     Medline TA:  IEEE Trans Vis Comput Graph     Country:  United States    
Other Details:
Languages:  eng     Pagination:  732-47     Citation Subset:  IM    
Affiliation:
School of Computer Science, Robotics Institute NSH, Carnege Mellon University, Pittsburgh, PA 15213-3890, USA. liyingus@yahoo.com
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MeSH Terms
Descriptor/Qualifier:
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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