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Cyclic Motion Analysis Using the Period Trace

  • Chapter
Motion-Based Recognition

Part of the book series: Computational Imaging and Vision ((CIVI,volume 9))

Abstract

Non-rigid motion analysis is complicated by a lack of general purpose rules or constraints governing how an object or scene evolves over time. In order to design practical algorithms, much of the work to date has focused on object-model-based techniques, such as interpretation of facial expressions, detection of human locomotion, cardiac image analysis, and gesture recognition. In contrast, repeating motions all share common temporal features that can be formally described and interpreted regardless of the particular object or scene that is moving. For instance, it is not necessary to recognize the runner or the motion in Fig. 1 in order to determine his stride frequency. Because many real-world motions repeat, e.g., a heart beating, an athlete running, and a wheel rotating, cyclic motion analysis techniques have broad applicability.

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Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of Wisconsin, Madison, WI, 53706, USA

    Steven M. Seitz & Charles R. Dyer

Authors
  1. Steven M. Seitz
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  2. Charles R. Dyer
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Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, Computer Science Department, University of Central Florida, 32816, Orlando, Florida, USA

    Mubarak Shah

  2. Electrical and Computer Engineering, University of California, San Diego, 92137, San Diego, California, USA

    Ramesh Jain

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© 1997 Springer Science+Business Media Dordrecht

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Seitz, S.M., Dyer, C.R. (1997). Cyclic Motion Analysis Using the Period Trace. In: Shah, M., Jain, R. (eds) Motion-Based Recognition. Computational Imaging and Vision, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8935-2_4

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  • DOI: https://doi.org/10.1007/978-94-015-8935-2_4

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4870-7

  • Online ISBN: 978-94-015-8935-2

  • eBook Packages: Springer Book Archive

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