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About

Ana Rebelo was born in Porto, Portugal, in 1985. She graduated in Mathematics Applied to Technology at School of Sciences of University of Porto, Portugal, in 2007. She received the M.Sc. degree in Mathematical Enginnering from School of Sciences of University of Porto, Portugal, in 2008. In 2008, she started her Ph.D. studies at School of Engineering, University of Porto, Portugal. She has been working since 2007 as a researcher at INESC TEC, an R&D institute affiliated to University of Porto, in the Visual Computing and Machine Intelligence Group (VCMI). Ana Rebelo was a Project Member of one FCT (Foundation of Science and Technology - Portugal) research project in the area of optical music recognition (OMR). She is currently working at Escola Superior Biotecnologia, Universidade Católica Portuguesa as an Invited Assistant Professor and she is a FCT PostDoctoral Researcher at INESC TEC. Her main research interests include computer vision, image processing, biometrics and document analysis.

Interest
Topics
Details

Details

001
Publications

2017

Multimodal Learning for Sign Language Recognition

Authors
Ferreira, PM; Cardoso, JS; Rebelo, A;

Publication
Pattern Recognition and Image Analysis - Lecture Notes in Computer Science

Abstract

2016

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

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

Publication
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).

2015

A new optical music recognition system based on combined neural network

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

Publication
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.

2015

A Fuzzy C-Means Algorithm for Fingerprint Segmentation

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

Publication
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.

2014

Classification of optical music symbols based on combined neural network

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

Publication
Proceedings - 2014 International Conference on Mechatronics and Control, ICMC 2014

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%. © 2014 IEEE.

Supervised
thesis

2015

Smart Image Retargeting Techniques

Author
Laura Figueiredo Ângelo

Institution
UP-FEUP

2015

Portuguese Sign Language recognition

Author
Pedro Miguel Martins Ferreira

Institution
UP-FEUP