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- W2132732015 abstract "This paper presents a new approach to evaluation of registration using a general discriminative learning model that is independent of the type of registration method. We select features by association of a registration with a set of metrics (pixel based, patch based and histogram based statistics) and learn a classifier that discriminates mis-registrations from correct registrations using Adaboost. Experiments on a set of wireless capsule endoscopy (CE) images and images extracted from minimally invasive surgical endoscopic video data are presented. Results show that the proposed method outperforms any single classifier." @default.
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- W2132732015 date "2009-06-01" @default.
- W2132732015 modified "2023-09-27" @default.
- W2132732015 title "Detecting registration failure" @default.
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- W2132732015 doi "https://doi.org/10.1109/isbi.2009.5193150" @default.
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