Bibliograph. Daten | Bonilla, Daniel Rubio: Automatic color segmentation enhancement based on world knowledge. Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Diplomarbeit Nr. 3016 (2010). 73 Seiten, englisch.
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CR-Klassif. | C.1.4 (Processor Architectures, Parallel Architectures) I.2.9 (Robotics) I.2.10 (Vision and Scene Understanding) I.4.8 (Image Processing and Computer Vision Scene Analysis) I.5.3 (Pattern Recognition Clustering)
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Kurzfassung | This project started with the aim of developing a faster, more precise and automatic system for calibrating the colors of the football field (the environment in which the robots, the “football players”, that participate in the RoboCup have to operate), so that the images taken from the camera (what the robots “see”) are segmented and can be later processed by another “world understanding” software. After a first evaluation of the different alternatives we had to develop this project for us to the wanted results, we decided that our best option was to use a clustering approach. It consists in grouping the colors in different clusters, and then select the clusters with more possibility to be a specific color representing each object of the real world.
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Volltext und andere Links | PDF (4042836 Bytes) Zugriff auf studentische Arbeiten aufgrund vorherrschender Datenschutzbestimmungen nur innerhalb der Fakultät möglich |
Abteilung(en) | Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Parallele Systeme
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Betreuer | Koch, Andreas |
Eingabedatum | 27. April 2010 |
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