Most of the work on 3D object recognition from range data has used an alignment-verification approach in which a specific 3D object is matched to an exact instance of the same object in a scene. This approach has been successfully used in industrial machine vision, but it is not capable of dealing with the complexities of recognizing classes of similar objects. This paper undertakes this task by proposing and testing a component-based methodology encompassing three main ingredients: 1) a new way of learning and extracting shape-class components from surface shape information; 2) a new shape representation called a symbolic surface signature that summarizes the geometric relationships among components; and 3) an abstract representation of shape classes formed by a hierarchy of classifiers that learn object-class parts and their spatial relationships from examples.
A new paradigm for recognizing 3-D objects from range data
Proceedings Ninth IEEE International Conference on Computer Vision ; 1126-1133 vol.2
01.01.2003
1963132 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
British Library Conference Proceedings | 2003
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