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2017 | OriginalPaper | Buchkapitel

5. Feature Detection

verfasst von : Klaus D. Toennies

Erschienen in: Guide to Medical Image Analysis

Verlag: Springer London

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Abstract

Image-based features such as key point locations or potential parts of object boundaries can be extracted from local image characteristics. Boundary parts are generated from 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. Region characteristics describe object boundaries just indirectly. They are often more robustly to compute as they often require only a rough estimate of the interior for their computation. Hence, they are more often used for object detection rather than for object delineation.

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Fußnoten
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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Metadaten
Titel
Feature Detection
verfasst von
Klaus D. Toennies
Copyright-Jahr
2017
Verlag
Springer London
DOI
https://doi.org/10.1007/978-1-4471-7320-5_5