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Segmentation

  • Chapter
Image Processing, Analysis and Machine Vision

Abstract

Image segmentation is one of the most important steps leading to the analysis of processed image data — its main goal is to divide an image into parts that have a strong correlation with objects or areas of the real world contained in the image. We may aim for complete segmentation, which results in a set of disjoint regions uniquely corresponding with objects in the input image, or for partial segmentation, in which regions do not correspond directly with image objects. To achieve a complete segmentation, cooperation with higher processing levels which use specific knowledge of the problem domain is necessary. However, there is a whole class of segmentation problems that can be successfully solved using lower level processing only. In this case, the image commonly consists of contrasted objects located on a uniform background — simple assembly tasks, blood cells, printed characters, etc. Here, a simple global approach can be used and the complete segmentation of an image into objects and background can be obtained. Such processing is context independent; no object-related model is used, and no knowledge about expected segmentation results contributes to the final segmentation.

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Authors and Affiliations

  1. University of Iowa, Iowa City, Iowa, USA

    Milan Sonka PhD

  2. Czech Technical University, Prague, Czech Republic

    Vaclav Hlavac PhD

  3. University of Leeds, Leeds, England

    Roger Boyle DPhil, MBCS, CEng

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  3. Roger Boyle DPhil, MBCS, CEng
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© 1993 Milan Sonka, Vaclav Hlavac and Roger Boyle

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Sonka, M., Hlavac, V., Boyle, R. (1993). Segmentation. In: Image Processing, Analysis and Machine Vision. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-3216-7_5

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