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The ability to acquire generic parts of 3D objects is an important step for recognition of objects in 3D space. Most natural articulated objects can be described as having a small number of salient 3D parts. Range data provides ready-to-use information to derive a reasonably stable and accurate differential structure of the surfaces, which allow us to track discontinuities between these parts. In this paper we present a mixed and comprehensive approach to segmenting and extracting prototypical parts from 3D articulated objects. This approach enables the acquisition of generic volumetric parts of a 3D object for the purpose of recognition.
Síntese de Imagens
Reconhecimento de Padrões
Visualização de Dados
Visão por Computador
Aplicações em Medicina
Modelagem e Visualização
Animação e Multimídia
Processamento de Imagens
titular original não localizado
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