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Beyond morphological size distribution

[+] Author Affiliations
Sébastien Lefèvre

University of Strasbourg—CNRS, Image Sciences, Computer Sciences, and Remote Sensing Laboratory (LSIIT), Pôle API, Boulevard Brant, 67412 Illkirch, France

J. Electron. Imaging. 18(1), 013010 (March 19, 2009). doi:10.1117/1.3099707
History: Received June 12, 2008; Revised January 13, 2009; Accepted February 04, 2009; Published March 19, 2009
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In the field of digital image processing, the description of image content is one of the most crucial tasks. Indeed, it is a mandatory step for various applications, such as industrial vision, medical imaging, content-based image retrieval, etc. The description of the image content is achieved through the computation of some predefined features, which can be performed at different scales. Among global features that describe the content of the whole image, the gray level histogram focuses on the distribution of gray levels within the image, while morphological features (e.g., the pattern spectrum) measure the distribution of object sizes in the image. Despite their broad interest, such morphological size-distribution features are limited due to their monodimensional nature. Our goal is to review multidimensional extensions of these features able to deal with complementary information (such as shape, orientation, spectral, intensity, or spatial information). Moreover, we illustrate each multidimensional feature by an illustrative example that shows their relevance compared to the standard morphological size distribution. These features can be seen as relevant solutions when the standard monodimensional features fail to accurately represent the image content.

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© 2009 SPIE and IS&T

Citation

Sébastien Lefèvre
"Beyond morphological size distribution", J. Electron. Imaging. 18(1), 013010 (March 19, 2009). ; http://dx.doi.org/10.1117/1.3099707


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