Detection of distorted frames in retinal video-sequences via machine learning

Radim Kolar; Ivana Liberdova; Jan Odstrcilik; Michal Hracho; Ralf P. Tornow

doi:10.1117/12.2284172

28 July 2017 Detection of distorted frames in retinal video-sequences via machine learning

Radim Kolar, Ivana Liberdova, Jan Odstrcilik, Michal Hracho, Ralf P. Tornow

Proceedings Volume 10413, Novel Biophotonics Techniques and Applications IV; 104130A (2017) https://doi.org/10.1117/12.2284172
Event: European Conferences on Biomedical Optics, 2017, Munich, Germany

Abstract

This paper describes detection of distorted frames in retinal sequences based on set of global features extracted from each frame. The feature vector is consequently used in classification step, in which three types of classifiers are tested. The best classification accuracy 96% has been achieved with support vector machine approach.

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Radim Kolar, Ivana Liberdova, Jan Odstrcilik, Michal Hracho, and Ralf P. Tornow "Detection of distorted frames in retinal video-sequences via machine learning", Proc. SPIE 10413, Novel Biophotonics Techniques and Applications IV, 104130A (28 July 2017); https://doi.org/10.1117/12.2284172

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Cited by 2 scholarly publications.

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KEYWORDS

Machine learning

Retinal scanning

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