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Publicações

Publicações por Ana Maria Rebelo

2015

A new optical music recognition system based on combined neural network

Autores
Wen, CH; Rebelo, A; Zhang, J; Cardoso, J;

Publicação
PATTERN RECOGNITION LETTERS

Abstract
Optical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we propose a new OMR system to recognize the music symbols without segmentation. We present a new classifier named combined neural network (CNN) that offers superior classification capability. We conduct tests on fifteen pages of music sheets, which are real and scanned images. The tests show that the proposed method constitutes an interesting contribution to OMR.

2014

Classification of Optical Music Symbols based on Combined Neural Network

Autores
Wen, CH; Rebelo, A; Zhang, J; Cardoso, J;

Publicação
2014 INTERNATIONAL CONFERENCE ON MECHATRONICS AND CONTROL (ICMC)

Abstract
In this paper, a new method for music symbol classification named Combined Neural Network (CNN) is proposed. Tests are conducted on more than 9000 music symbols from both real and scanned music sheets, which show that the proposed technique offers superior classification capability. At the same time, the performance of the new network is compared with the single Neural Network (NN) classifier using the same music scores. The average classification accuracy increased more than ten percent, reaching 98.82%.

2013

Global Constraints for Syntactic Consistency in OMR: An Ongoing Approach

Autores
Rebelo, A; Marcal, ARS; Cardoso, JS;

Publicação
IMAGE ANALYSIS AND RECOGNITION

Abstract
Optical Music Recognition (OMR) systems are an indispensable tool to transform the paper-based music scores and manuscripts into a machine-readable symbolic format. A system like this potentiates search, retrieval and analysis. One of the problematic stages is the musical symbols detection where operations to localize and to isolate musical objects are developed. The complexity is caused by printing and digitalization, as well as the paper degradation over time. Distortions inherent in staff lines, broken, connected and overlapping symbols, differences in sizes and shapes, noise, and zones of high density of symbols is even worst when we are dealing with handwritten music scores. In this paper the exploration of an optimization approach to support semantic and syntactic consistency after the music symbols extraction phase is proposed. The inclusion of this ongoing technique can lead to better results and encourage further experiences in the field of handwritten music scores recognition.

2013

Staff line Detection and Removal in the Grayscale Domain

Autores
Rebelo, A; Cardoso, JS;

Publicação
2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)

Abstract
The detection of staff lines is the first step of most Optical Music Recognition (OMR) systems. Its great significance derives from the ease with which we can then proceed with the extraction of musical symbols. All OMR tasks are usually achieved using binary images by setting thresholds that can be local or global. These techniques however, may remove relevant information of the music sheet and introduce artifacts which will degrade results in the later stages of the process. It arises therefore a need to create a method that reduces the loss of information due to the binarization. The baseline for the methodology proposed in this paper follows the shortest path algorithm proposed in [1]. The concept of strong staff pixels (SSP's), which is a set of pixels with a high probability of belonging to a staff line, is proposed to guide the cost function. The SSP allows to overcome the results of the binary based detection and to generalize the binary framework to grayscale music scores. The proposed methodology achieves good results.

2015

A Fuzzy C-Means Algorithm for Fingerprint Segmentation

Autores
Ferreira, PM; Sequeira, AF; Rebelo, A;

Publicação
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)

Abstract
Fingerprint segmentation is a crucial step of an automatic fingerprint identification system, since an accurate segmentation promote both the elimination of spurious minutiae close to the foreground boundaries and the reduction of the computation time of the following steps. In this paper, a new, and more robust fingerprint segmentation algorithm is proposed. The main novelty is the introduction of a more robust binarization process in the framework, mainly based on the fuzzy C-means clustering algorithm. Experimental results demonstrate significant benchmark progress on three existing FVC datasets.

2016

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

Autores
Wen, CH; Zhang, J; Rebelo, A; Cheng, FY;

Publicação
PLOS ONE

Abstract
Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

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