Abstract
During the last few years, the development of nanomaterials increases in many fields of sciences (biology, material, medicine...) to control physical-chemical properties. Among these materials, carbon black is the oldest one and is widely used as reinforcement filler in rubber products. Nevertheless, the interaction between nanoparticles and polymer matrix is poorly understood. In other words carbon black aggregate’s characteristics are usually obtained by poorly official indirect analyses. This article presents an image processing chain allowing subsequent characterization of the carbon black aggregates. A database of several hundred samples of carbon black images has been collected using transmission electron microscopy. A significant selection of images has been manually expertised for ground truth. Using supervised evaluation criteria, a comparative study is performed with state-of-the-art carbon black segmentation algorithms, highlighting the good performances of the proposed algorithm.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Luengo, L., Laurent, H., Treuillet, S., Jolivet, I., Gomez, E. (2013). Unsupervised Segmentation for Transmission Imaging of Carbon Black. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_46
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DOI: https://doi.org/10.1007/978-3-319-02895-8_46
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