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Dense Motion Analysis

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Part of the Undergraduate Topics in Computer Science book series (UTICS)

Abstract

This chapter discusses optic flow, the standard representation in computer vision for dense motion. Every pixel is labelled by a motion vector, indicating the change in image data from time t to time t+1. Sparse motion analysis (also known as tracking) will be a subject in Chap. 9.

Keywords

  • Optical Flow
  • Motion Vector
  • Optical Flow Algorithm
  • Dense Motion
  • Asymmetric Difference

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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  • DOI: 10.1007/978-1-4471-6320-6_4
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Notes

  1. 1.

    We could skip (4.28) to (4.30) as the vector a is orthogonal to the line by definition: it joins the origin with its orthogonal projection on the line (the property of the nearest point). Being orthogonal to the line, it must be parallel to the gradient vector.

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Klette, R. (2014). Dense Motion Analysis. In: Concise Computer Vision. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-6320-6_4

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  • DOI: https://doi.org/10.1007/978-1-4471-6320-6_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6319-0

  • Online ISBN: 978-1-4471-6320-6

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