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2D/3D Image Data Analysis for Object Tracking and Classification

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Advances in Machine Learning and Data Analysis

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 48))

Abstract

Object tracking and classification is of utmost importance for different kinds of applications in computer vision. In this chapter, we analyze 2D/3D image data to address solutions to some aspects of object tracking and classification. We conclude our work with a real time hand based robot control with promising results in a real time application, even under challenging varying lighting conditions.

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Notes

  1. 1.

    For example PMD-40K (200 ×200 pixels), Swissranger 4000 (176 ×144 pixels) and ZCam-prototype (320 ×480 pixels).

  2. 2.

    Levenberg-Marquardt algorithm; the optimization criterion is the sum of squared distances of the individual points.

References

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Acknowledgments

This work has been funded by German Research Foundation (DFG) under contract number LO 455/10-2 which is gratefully appreciated.

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Authors and Affiliations

  1. Center for Sensor Systems (ZESS), University of Siegen, Paul-Bonatz-Str.9-11, D57068, Siegen, Germany

    Seyed Eghbal Ghobadi, Omar Edmond Loepprich, Oliver Lottner, Klaus Hartmann, Wolfgang Weihs & Otmar Loffeld

Authors
  1. Seyed Eghbal Ghobadi
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  2. Omar Edmond Loepprich
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  3. Oliver Lottner
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  4. Klaus Hartmann
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  5. Wolfgang Weihs
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  6. Otmar Loffeld
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Corresponding author

Correspondence to Seyed Eghbal Ghobadi .

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Editors and Affiliations

  1. Dept. Chemical Engineering, California State University, Long Beach, 90840, California, USA

    Mahyar A. Amouzegar

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Ghobadi, S.E., Loepprich, O.E., Lottner, O., Hartmann, K., Weihs, W., Loffeld, O. (2010). 2D/3D Image Data Analysis for Object Tracking and Classification. In: Amouzegar, M. (eds) Advances in Machine Learning and Data Analysis. Lecture Notes in Electrical Engineering, vol 48. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3177-8_1

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  • DOI: https://doi.org/10.1007/978-90-481-3177-8_1

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-3176-1

  • Online ISBN: 978-90-481-3177-8

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