Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features

  • Michael Kemmler
  • Erik Rodner
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5742)


It has been highlighted by many researchers, that the use of context information as an additional cue for high-level object recognition is important to close the gap between human and computer vision. We present an approach to context extraction in the form of global features for place recognition. Based on an uncalibrated combination of range data of a time-of-flight (ToF) camera and images obtained from a visual sensor, our system is able to classify the environment in predefined places (e.g. kitchen, corridor, office) by representing the sensor data with various global features. Besides state-of-the-art feature types, such as power spectrum models and Gabor filters, we introduce histograms of surface normals as a new representation of range images. An evaluation with different classifiers shows the potential of range data from a ToF camera as an additional cue for this task.


Support Vector Machine Hide Markov Model Mobile Robot Object Recognition Recognition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michael Kemmler
    • 1
  • Erik Rodner
    • 1
  • Joachim Denzler
    • 1
  1. 1.Chair for Computer VisionFriedrich Schiller University of JenaGermany

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