3 research outputs found
Image operator learning coupled with CNN classification and its application to staff line removal
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 when
using 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 solution
outperforms 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.Comment: To appear in ICDAR 201