| Full text | |
| Author(s): |
Julca-Aguilar, Frank D.
;
Hirata, Nina S. T.
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), VOL 1; v. N/A, p. 6-pg., 2017-01-01. |
| 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 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. (AU) | |
| FAPESP's process: | 15/17741-9 - Combination of local and global features in image operator learning |
| Grantee: | Nina Sumiko Tomita Hirata |
| Support Opportunities: | Regular Research Grants |