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• W2515243097 abstract "Feature extraction plays a key role in pattern recognition. Here we present a rotation-invariant feature extraction methodology based on a structural approach using co-occurrence statistics. We assessed the performance of our method comparing it to a gray-level co-occurrence matrix, local binary patterns and invariant moments in pattern recognition experiments. The results show that the proposed method provides an efficient and fast way to analyze digital images." @default.
• W2515243097 created "2016-09-16" @default.
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• W2515243097 date "2016-12-01" @default.
• W2515243097 modified "2023-10-12" @default.
• W2515243097 title "Rotation-invariant feature extraction using a structural co-occurrence matrix" @default.
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• W2515243097 doi "https://doi.org/10.1016/j.measurement.2016.08.012" @default.
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