Abstract
In this paper we present a novel technique for unsupervised texture segmentation of wireless capsule endoscopic images of the human gastrointestinal tract. Our approach integrates local polynomial approximation algorithm with the well-founded methods of color texture analysis and clustering (k-means) leading to a robust segmentation procedure which produces fine-grained segments well matched to the image contents.
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Klepaczko, A., Szczypiński, P., Daniel, P., Pazurek, M. (2010). Local Polynomial Approximation for Unsupervised Segmentation of Endoscopic Images. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15907-7_5
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DOI: https://doi.org/10.1007/978-3-642-15907-7_5
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