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Robust Multi-Algorithm Object Recognition Using Machine Learning Methods

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Title Robust Multi-Algorithm Object Recognition Using Machine Learning Methods
Title of Series Robotics - Hochschule Ravensburg-Weingarten
Author Ertel, Wolfgang
Contributors The Institute for Artificial Intelligence (IKI)
Autonome mobile Serviceroboter Standort Ravensburg-Weingarten, Zentrum für angewandte Forschung an Fachhochschulen
Fromm, Tobias
Staehle, Benjamin
License CC Attribution - NonCommercial - NoDerivatives 3.0 Germany:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
DOI 10.5446/30632
Publisher Hochschule Ravensburg-Weingarten
Release Date 2012
Language English

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Subject Area Engineering
Abstract Object recognition in a household environment performed by a service robot. The objects are recognized by shape, texture and color-based algorithms whose outputs are then combined to create an object hypothesis. This works score-based, meaning that arbitrary algorithms and recognition frameworks can be combined without changing their outputs. In contrary to similar approaches, the proposed method automatically selects which algorithms to use and how to weigh their outputs. Reference: T. Fromm, B. Staehle, W. Ertel: Robust Multi-Algorithm Object Recognition Using Machine Learning Methods. IEEE International Conference on Multisensor Fusion and Information Integration, Hamburg, Germany, 2012.
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