English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/5520
COMPARTIR / IMPACTO:
Estadísticas
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Título

A Tale of Two Object Recognition Methods for Mobile Robots

AutorRamisa, Arnau; Vasudevan, Shrihari; Scharamuzza, Davide; López de Mántaras, Ramón ; Siegwart, Roland
Palabras claveArtificial Intelligence
Object recognition
Mobile robot
Fecha de publicación2008
EditorSpringer
CitaciónComputer Vision Systems, 6th International Conference, ICVS 2008 Santorini, Greece, May 12-15, 2008 Proceedings. Lecture Notes in Computer Science, vol.5008, p.p.:353-362, Springer, 2008.
ResumenObject recognition is a key feature for building robots capable of moving and performing tasks in human environments. However, current object recognition research largely ignores the problems that the mobile robots context introduces. This work addresses the problem of applying these techniques to mobile robotics in a typical household scenario. We select two state-of-the-art object recognition methods, which are suitable to be adapted to mobile robots, and we evaluate them on a challenging dataset of typical household objects that caters to these requirements. The different advantages and drawbacks found for each method are highlighted, and some ideas for extending them are proposed. Evaluation is done comparing the number of detected objects and false positives for both approaches.
DescripciónThis original publication is available at www.springerlink.com
URIhttp://hdl.handle.net/10261/5520
DOI10.1007/978-3-540-79547-6_34
ISBN978-3-540-79546-9
ISSN0302-9743
1611-3349
Aparece en las colecciones: (IIIA) Comunicaciones congresos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
icvs2008.pdf256,65 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.