Title:
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3D modelling of leaves from color and ToF data for robotized plant measuring
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Author:
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Alenyà Ribas, Guillem; Dellen, Babette; Torras, Carme
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Other authors:
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Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
Abstract:
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Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Robòtica -Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Three-dimensional display systems -Pattern recognition systems -Computer vision -Cameras -Image color analysis -Image segmentation -Robot kinematics -Robot vision systems -Three dimensional displays -Reconeixement de formes (Informàtica) -Visualització tridimensional (Informàtica) -Visió per ordinador -Classificació INSPEC::Pattern recognition::Computer vision |
Rights:
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Document type:
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Article - Published version Conference Object |
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