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Image Segmentation

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

Abstract

In this chapter we explain special approaches for image binarization and segmentation of still images or video frames, in the latter case with attention to ensuring temporal consistency. We discuss mean-shift segmentation in detail. We also provide a general view on image segmentation as (another) labelling example in computer vision, introduce segmentation this way from an abstract point of view, and discuss belief-propagation solutions for this labelling framework.

Keywords

  • Image Segmentation
  • Pixel Location
  • Lower Envelope
  • Temporal Consistency
  • Subsequent Frame

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_5
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Notes

  1. 1.

    Figure from [B.-S. Shin, J. Russell, Y. Zheng, and R. Klette. Improved segmentation for footprint recognition of small mammals. In Proc. IVCNZ, an ACM publication, Nov. 2012].

  2. 2.

    See Fig. 10.11 in the book [G. Bradski and A. Kaehler. Learning OpenCV. O’Reilly, Beijing, 2008] for a graphical illustration of this equation. Mean-shift is there not discussed for clustering in feature space but for tracking in pixel domain.

  3. 3.

    Consider increasing functions f and g from the set \(\Bbb{N}\) of natural numbers into the set \(\Bbb{R}^{+}\) of positive reals. We have f(n)∈𝒪(g(n)) iff there exist a constant c>0 and an n 0>0 such that f(n)≤cg(n) for all nn 0.

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Klette, R. (2014). Image Segmentation. In: Concise Computer Vision. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-6320-6_5

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  • DOI: https://doi.org/10.1007/978-1-4471-6320-6_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6319-0

  • Online ISBN: 978-1-4471-6320-6

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