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Feature Detection

  • Chapter
Guide to Medical Image Analysis

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Data-based features in images such as key point locations or potential parts of object boundaries can be extracted from local image characteristics. Boundary parts are generated from the results of an edge enhancement step while key point locations are local extrema of some local object property. Features may also be computed from samples of an object’s boundary or interior.

Potential object boundary parts are used for detecting or delineating objects in images. Key points may, in some simple cases, also be used to detect objects. In most cases, however, object characteristics are too complex to be captured by the attributes of a key point. They can be important attributes nonetheless. Key points define an object-dependent reference system in which they may be used to map objects of the same class onto each other.

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Notes

  1. 1.

    This can be difficult, however, since texture measures are estimated from a local neighborhood that is assumed to have the same texture. This is not true at texture boundaries.

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Authors and Affiliations

  1. Computer Science Department, ISG, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Prof. Klaus D. Toennies

Authors
  1. Prof. Klaus D. Toennies
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© 2012 Springer-Verlag London Limited

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Toennies, K.D. (2012). Feature Detection. In: Guide to Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2751-2_5

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  • DOI: https://doi.org/10.1007/978-1-4471-2751-2_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2750-5

  • Online ISBN: 978-1-4471-2751-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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