Abstract
In this paper, a method of elongated structure detection is presented. In general, this is not a trivial task since standard image segmentation techniques require usually quite complex procedures to incorporate the information about the expected shape of the segments. The presented approach may be an interesting alternative for them. In its first phase, it changes the representation of the image. Instead of a set of pixels, the image is described by a set of ellipses representing fragments of the regions of similar color. This representation is obtained using cross-entropy clustering (CEC) method. The second phase analyses geometrical and spatial relationships between ellipses to select those of them that form an elongated structure within an acceptable range of its width. Both phases are elements of hierarchical active partition framework which iteratively collects semantic information about image content.
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Implementation of the CEC algorithm for the Project R—a free software environment for statistical computing and graphics—is available at [6].
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Acknowledgments
This project has been funded with support from the National Science Centre, Republic of Poland, decision number DEC-2012/05/D/ST6/03091.
\(-\) The work of Przemysław Spurek was supported by the National Centre of Science (Poland) [grant no. 2013/09/N/ST6/01178].
\(-\) The work of Krzysztof Misztal was supported by the National Centre of Science (Poland) [grant no. 2012/07/N/ST6/02192].
\(-\) The work of Jacek Tabor was supported by the National Centre of Science (Poland) [grant no. 2014/13/B/ST6/01792].
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Tomczyk, A., Spurek, P., Podgórski, M., Misztal, K., Tabor, J. (2016). Detection of Elongated Structures with Hierarchical Active Partitions and CEC-Based Image Representation. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_15
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