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Ansichtsbasierte Erkennung Komplexer 3d-Objekte Aus Tiefenbildern

Ansichtsbasierte Erkennung Komplexer 3d-Objekte Aus Tiefenbildern

          
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About the Book

Die Erkennung und Lokalisation von Objekten ist eine der zentralen Aufgaben des maschinellen Bildverstehens. Potentielle Anwendungsgebiete reichen von der Qualitatskontrolle uber Fertigungs- und Lagerhaltungsszenarien bis zu einer Vielzahl von Einsatzen in der Robotik. Die aktuellen Forschungsaktivitaten konzentrieren sich auf die Erkennung von 3D-Objekten, wobei noch viele Verfahren Einschrankungen bezuglich der Komplexitat der Objekte verlangen. Die Sensordaten spielen eine Schlusselrolle bei der Implementierung eines Objekterkennungssystems. Statt der ublichen Grauwert- oder Farbbilder werden in dieser Arbeit ausschliesslich Tiefendaten verwendet. Moderne Sensortechnik wird fur die zuverlassige und schnelle Gewinnung der Daten eingesetzt. Durch die Reprasentation von Entfernungsinformation sind Tiefendaten besonders gut zur Erkennung von 3D-Objekten geeignet. Tiefendaten erfordern allerdings neue Auswerteverfahren, bekannte Verfahren fur 2D-Bilder lassen sich nur zum Teil ubertragen. Der wesentliche wissenschaftliche Beitrag der vorliegenden Dissertation liegt in der Realisierung eines neuartigen ansichtsbasierten Objekterkennungsverfahrens. Konsequent werden die Vorteile von Tiefendaten, sowohl bei der Modellierung, als auch bei der Auswahl der Merkmale zur Erkennung genutzt. Die Modellansichten werden automatisch aus den CAD-Beschreibungen der Objekte generiert. Verlassliche 3D-Merkmale in Form von Normalenvektoren, Krummungen und Distanzwerten werden fur jeden einzelnen Punkt berechnet. Durch die segmentierungsfreie Strategie wird das Verfahren robust gegenuber partiellen Verdeckungen und ermoglicht die Erkennung beliebig geformter 3D-Objekte. Die Invarianzeigenschaften der ausgewahlten Merkmale vereinfachen die Erkennung erheblich. Die Merkmale werden in multidimensionalen Histogrammen zusammengefasst. Dadurch reduziert sich die Datenmenge, das Verfahren wird sehr schnell und ist fur praktische Anwendungen geeignet. Das Matching selbst basiert auf einem statistischen Modell der Objekte. Verschiedene Methoden zum Vergleich von Modell- und Szenendaten werden ausfuhrlich untersucht. Die Leistungsfahigkeit des Ansatzes wird durch eine Vielzahl von experimentellen Untersuchungen an Objekten mit Freiformflachen nachgewiesen.


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Product Details
  • ISBN-13: 9783832507336
  • Publisher: Logos Verlag Berlin
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Spine Width: 0 mm
  • Width: 145 mm
  • ISBN-10: 3832507337
  • Publisher Date: 20 Nov 2004
  • Height: 210 mm
  • No of Pages: 214
  • Series Title: German
  • Weight: 700 gr


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