| Records |
| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
From Optical Music Recognition to Handwritten Music Recognition: a Baseline |
Type |
Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
123 |
Issue |
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Pages |
1-8 |
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| Abstract |
Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. |
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DAG; 600.097; 601.302; 601.330; 600.140; 600.121 |
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no |
Call Number  |
Admin @ si @ BRC2019 |
Serial |
3275 |
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| Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
| Title |
Multispectral Piecewise Planar Stereo using Manhattan-World Assumption |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
34 |
Issue |
1 |
Pages |
52-61 |
| Keywords |
Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images |
| Abstract |
This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. |
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ADAS; 600.054; 600.055; 605.203 |
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no |
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Admin @ si @ BLS2013 |
Serial |
2245 |
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| Author |
Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
| Title |
Fast: Facilitated and accurate scene text proposals through fcn guided pruning |
Type |
Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
119 |
Issue |
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Pages |
112-120 |
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Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. |
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DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ BGN2019 |
Serial |
3342 |
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| Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
| Title |
Minimal Design of Error-Correcting Output Codes |
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Journal Article |
| Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
33 |
Issue |
6 |
Pages |
693-702 |
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Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
| Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Elsevier |
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0167-8655 |
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MILAB; OR;HuPBA;MV |
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no |
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Admin @ si @ BEB2011a |
Serial |
1800 |
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| Author |
Eduardo Aguilar; Petia Radeva |
| Title |
Uncertainty-aware integration of local and flat classifiers for food recognition |
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Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
136 |
Issue |
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Pages |
237-243 |
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Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen. |
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MILAB; no proj |
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no |
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Admin @ si @ AgR2020 |
Serial |
3525 |
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A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |
| Title |
Topological principal component analysis for face encoding and recognition |
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Journal Article |
| Year |
2001 |
Publication |
Pattern Recognition Letters |
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PRL |
| Volume |
22 |
Issue |
6-7 |
Pages |
769–776 |
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IF: 0.552 |
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ADAS;OR;MV |
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ADAS @ adas @ PVL2001 |
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155 |
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| Author |
Fadi Dornaika; Angel Sappa |
| Title |
Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression |
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Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
30 |
Issue |
5 |
Pages |
535–543 |
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This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes. |
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Elsevier Science Inc. |
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0167-8655 |
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ADAS |
Approved |
no |
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ADAS @ adas @ DoS2009a |
Serial |
1115 |
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| Author |
Fadi Dornaika; Angel Sappa |
| Title |
Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models |
Type |
Journal Article |
| Year |
2007 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
28 |
Issue |
15 |
Pages |
2116-2126 |
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ADAS |
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ADAS @ adas @ DoS2007c |
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877 |
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| Author |
Jaume Amores; N. Sebe; Petia Radeva |
| Title |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
Type |
Journal Article |
| Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
27 |
Issue |
3 |
Pages |
201–209 |
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ADAS;MILAB |
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no |
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ADAS @ adas @ ASR2006 |
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643 |
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| Author |
Jaume Amores; Petia Radeva |
| Title |
Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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Journal Article |
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2005 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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26 |
Issue |
11 |
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1720–1731 |
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IF: 1.138 |
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ADAS;MILAB |
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no |
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ADAS @ adas @ AmR2005b |
Serial |
592 |
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