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- W2962812619 abstract "Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue whenusing learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solutionoutperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site." @default.
- W2962812619 created "2019-07-30" @default.
- W2962812619 creator A5011625832 @default.
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- W2962812619 date "2017-11-01" @default.
- W2962812619 modified "2023-09-27" @default.
- W2962812619 title "Image Operator Learning Coupled with CNN Classification and Its Application to Staff Line Removal" @default.
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- W2962812619 doi "https://doi.org/10.1109/icdar.2017.18" @default.
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