Abstract
The present chapter deals with partitioning or segmentation of images into regions of different texture. We shall not make precise what ‘texture’ means; this will be indicated in Chapter 15. We just want to tell different textures apart, in contrast to texture classification addressed in Chapter 16: a segmentor subdivides the image; a classifier recognizes individual segments and assigns them to particular classes. Nevertheless, partitioning is also useful for classification. A ‘region classifier’ which decides to which texture a region belongs can be put to work after partitioning. This is helpful in situations where there are no pre-defined classes; perhaps, these can be identified after partitioning.
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© 2003 Springer-Verlag Berlin Heidelberg
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Winkler, G. (2003). Partitioning. In: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Applications of Mathematics, vol 27. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55760-6_15
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DOI: https://doi.org/10.1007/978-3-642-55760-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-62911-2
Online ISBN: 978-3-642-55760-6
eBook Packages: Springer Book Archive