Thesis etd-10072003-012258 |
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Thesis type
Tesi di laurea vecchio ordinamento
Author
Tardella, Massimo
email address
maxtard@libero.it
URN
etd-10072003-012258
Thesis title
Tecniche di apprendimento per la visione artificiale di un sistema robotico biomedico per l'assistenza personale.
Department
INGEGNERIA
Course of study
INGEGNERIA ELETTRONICA
Supervisors
relatore Prof. Landini, Luigi
Keywords
- DirectSVM
- robotica per l'assistenza personale
- support vector machines
- SVM
- visione artificiale
Graduation session start date
23/10/2003
Availability
Withheld
Release date
23/10/2043
Summary
In robotic systems, the use of vision, together with other perception systems, is motivated by the need to increase the versatility and the number of performable applications. If proximity, feel and force perception are significant to increase the performance of executions in traditional manufactory applications, vision is acknowledged as the most powerful among the perceptual abilities of a service robot.
The purpose of this work is to develop a system providing an assistive robot1 with visual capabilities for object localization and recognition based on a learning technique that increases the flexibility and robustness of the system and its generalization capacity. This work was almost entirely developed within the research group developing the Care-O-bot [30] system at the Fraunhofer IPA2, in order to explore the potentialities of a recent learning technique (the Support Vector Machines) to solve the problem of object detection and recognition for such a system.
The purpose of this work is to develop a system providing an assistive robot1 with visual capabilities for object localization and recognition based on a learning technique that increases the flexibility and robustness of the system and its generalization capacity. This work was almost entirely developed within the research group developing the Care-O-bot [30] system at the Fraunhofer IPA2, in order to explore the potentialities of a recent learning technique (the Support Vector Machines) to solve the problem of object detection and recognition for such a system.
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