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Diatom Feature Extraction and Classification

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Part of the Developments in Applied Phycology book series (DAPH,volume 10)

Abstract

This chapter presents the most relevant image processing techniques and algorithms related to computing features that are able to characterize diatoms as objects in the computer vision field for further analysis and classification. For this purpose, a wide revision of the most important contributions to diatom classification is performed. Moreover, features that have been found to be suitable for this task are covered. Later on, the reader will find the main techniques for diatom classification for the two paradigms that are used nowadays: machine learning with classical methods that rely on previously selected features, or deep learning, which learns the features from the images automatically.

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  • DOI: 10.1007/978-3-030-39212-3_9
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Notes

  1. 1.

    http://rbg-web2.rbge.org.uk/ADIAC/.

  2. 2.

    A standardized feature is a feature rescaled to have mean =  0 and std =  1.

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Correspondence to Noelia Vallez .

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Appendix

Appendix

In this section, the reader can find the links of some of the methods explained in this chapter.

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Vallez, N., Pedraza, A., Sánchez, C., Salido, J., Deniz, O., Bueno, G. (2020). Diatom Feature Extraction and Classification. In: Cristóbal, G., Blanco, S., Bueno, G. (eds) Modern Trends in Diatom Identification. Developments in Applied Phycology, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39212-3_9

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