Mikhailova, Inna (2003)
Saliency-based Gaze Direction Control for Active Vision.
Technische Universität Darmstadt
Diplom- oder Magisterarbeit, Bibliographie
Kurzbeschreibung (Abstract)
The research field of robotics exists now for over fifty years, but the robots are still not able to deal with simple tasks that can be easily accomplished by two year old children. Writing sophisticated algorithms, tuning the parameters, building extra-sensitive sensors results in demonstrations that are successful only in a confined experimental environment and fail in a natural one. How could robotics overcome this situation? In recent years researchers are more and more renouncing rigid algorithmic architectures that contain too much of explicit knowledge. These architectures are replaced with flexible ones, such as for example neural networks. It is surely the right way for coming closer to the performance of the human brain. Indeed, an autonomous robot needs structures that are adaptive, self-organised, able to learn and to interact with the environment. Following this philosophy we implement a gaze direction control for active vision.
| Typ des Eintrags: | Diplom- oder Magisterarbeit |
|---|---|
| Erschienen: | 2003 |
| Autor(en): | Mikhailova, Inna |
| Art des Eintrags: | Bibliographie |
| Titel: | Saliency-based Gaze Direction Control for Active Vision |
| Sprache: | Englisch |
| Publikationsjahr: | 2003 |
| Ort: | Technische Universitaet Darmstadt |
| Kurzbeschreibung (Abstract): | The research field of robotics exists now for over fifty years, but the robots are still not able to deal with simple tasks that can be easily accomplished by two year old children. Writing sophisticated algorithms, tuning the parameters, building extra-sensitive sensors results in demonstrations that are successful only in a confined experimental environment and fail in a natural one. How could robotics overcome this situation? In recent years researchers are more and more renouncing rigid algorithmic architectures that contain too much of explicit knowledge. These architectures are replaced with flexible ones, such as for example neural networks. It is surely the right way for coming closer to the performance of the human brain. Indeed, an autonomous robot needs structures that are adaptive, self-organised, able to learn and to interact with the environment. Following this philosophy we implement a gaze direction control for active vision. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik |
| Hinterlegungsdatum: | 26 Jun 2019 11:50 |
| Letzte Änderung: | 26 Jun 2019 11:50 |
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