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

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.
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