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• W2078386876 abstract "While local patches recognition is a key component of modern approaches to affine transformation detection and object detection, existing learning-based approaches just identify the patches based on a set of randomly picked and combined binary features, which will lose some strong correlations between features and can not provide stable and remarkable identification ability. In this paper, we proposed a method that select and organize the features in a Multilayer Ferns structure, and show that it is both faster in the run-time processing and more powerful in the identification ability than state-of-the-art ad hoc approaches." @default.
• W2078386876 created "2016-06-24" @default.
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• W2078386876 date "2010-12-01" @default.
• W2078386876 modified "2023-09-27" @default.
• W2078386876 title "Multilayer Ferns: A Learning-based Approach of Patch Recognition and Homography Extraction" @default.
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• W2078386876 doi "https://doi.org/10.1109/icmla.2010.36" @default.
• W2078386876 hasPublicationYear "2010" @default.
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