28 July 2017 Detection of distorted frames in retinal video-sequences via machine learning
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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, Radim Kolar, Ivana Liberdova, Ivana Liberdova, Jan Odstrcilik, Jan Odstrcilik, Michal Hracho, Michal Hracho, Ralf P. Tornow, 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); doi: 10.1117/12.2284172; https://doi.org/10.1117/12.2284172
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