PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

THIS WEBPAGE IS DEPRECATED - please visit our new website

D. Pangercic, V. Haltakov , M. Beetz
Fast and Robust Object Detection in Household Environments Using Vocabulary Trees with SIFT Descriptors
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Active Semantic Perception and Object Search in the Real World, 2011 (bib)

In this paper we describe the ODUfinder, a novel perception system for autonomous service robots acting in human living environments. The perception system enables robots to detect and recognize large sets of textured objects of daily use. Efficiency, robustness, and a high detection rate are achieved through the combination of modern text retrieval methods that are successfully used for indexing huge sets of web pages and state-of-the-art robot vision methods for object recognition. The result is a robot object detection and recognition system that, with an accuracy rate of more than 80%, can recognize thousands of objects by learning and using vocabulary trees of SIFT descriptors.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif