| Records |
Author  |
Arnau Ramisa; Shrihari Vasudevan; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
| Title |
Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications |
Type |
Conference Article |
| Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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| Volume |
202 |
Issue |
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Pages |
9-18 |
| Keywords |
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| Abstract |
General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects. |
| Address |
Cardona, Spain |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0922-6389 |
ISBN |
978-1-60750-061-2 |
Medium |
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| Area |
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Expedition |
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Conference |
CCIA |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RVA2009 |
Serial |
1248 |
| Permanent link to this record |
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| Author |
Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
| Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
Type |
Conference Article |
| Year |
2007 |
Publication |
4th International Conference on Informatics in Control, Automation and Robotics |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
292–297 |
| Keywords |
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| Abstract |
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| Address |
Angers (France) |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICINCO |
| Notes |
RV;ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RLA2007 |
Serial |
900 |
| Permanent link to this record |
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| Author |
Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras |
| Title |
The IIIA30 MObile Robot Object Recognition Datset |
Type |
Conference Article |
| Year |
2011 |
Publication |
11th Portuguese Robotics Open |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones. |
| Address |
Lisboa |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
Robotica |
| Notes |
RV;ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RAV2011 |
Serial |
1777 |
| Permanent link to this record |
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| Author |
Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras |
| Title |
Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot |
Type |
Journal Article |
| Year |
2012 |
Publication |
Journal of Intelligent and Robotic Systems |
Abbreviated Journal |
JIRC |
| Volume |
68 |
Issue |
2 |
Pages |
185-208 |
| Keywords |
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| Abstract |
This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Netherlands |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0921-0296 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RAV2012 |
Serial |
2150 |
| Permanent link to this record |
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| Author |
Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
| Title |
Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas |
Type |
Journal Article |
| Year |
2011 |
Publication |
Journal of Intelligent and Robotic Systems |
Abbreviated Journal |
JIRC |
| Volume |
64 |
Issue |
3-4 |
Pages |
625-649 |
| Keywords |
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| Abstract |
Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Netherlands |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0921-0296 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
RV;ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RGA2011 |
Serial |
1728 |
| Permanent link to this record |
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| Author |
Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo |
| Title |
Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors |
Type |
Conference Article |
| Year |
2008 |
Publication |
IEEE International Conference on Robotics and Automation, |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
538–543 |
| Keywords |
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| Abstract |
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| Address |
Pasadena; CA; USA |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICRA |
| Notes |
RV;ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RTL2008 |
Serial |
1144 |
| Permanent link to this record |
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| Author |
Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
| Title |
Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors |
Type |
Journal Article |
| Year |
2009 |
Publication |
Autonomous Robots |
Abbreviated Journal |
AR |
| Volume |
27 |
Issue |
4 |
Pages |
373-385 |
| Keywords |
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| Abstract |
This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization. |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0929-5593 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RTA2009 |
Serial |
1245 |
| Permanent link to this record |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
Optical Music Recognition by Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
25-26 |
| Keywords |
Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
| Abstract |
Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 600.097; 601.302; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2017 |
Serial |
3056 |
| Permanent link to this record |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
Optical Music Recognition by Long Short-Term Memory Networks |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
Abbreviated Journal |
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| Volume |
11009 |
Issue |
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Pages |
81-95 |
| Keywords |
Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
| Abstract |
Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
A. Fornes, B. Lamiroy |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-3-030-02283-9 |
Medium |
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| Area |
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Expedition |
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Conference |
GREC |
| Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2018 |
Serial |
3227 |
| Permanent link to this record |
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| 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 |
| Keywords |
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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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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG; 600.097; 601.302; 601.330; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2019 |
Serial |
3275 |
| Permanent link to this record |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
Towards the recognition of compound music notes in handwritten music scores |
Type |
Conference Article |
| Year |
2016 |
Publication |
15th international conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising. |
| Address |
Shenzhen; China; October 2016 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2167-6445 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICFHR |
| Notes |
DAG; 600.097 |
Approved |
no |
| Call Number |
Admin @ si @ BRF2016 |
Serial |
2903 |
| Permanent link to this record |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
A Starting Point for Handwritten Music Recognition |
Type |
Conference Article |
| Year |
2018 |
Publication |
1st International Workshop on Reading Music Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
5-6 |
| Keywords |
Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
| Abstract |
In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
| Address |
Paris; France; September 2018 |
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Thesis |
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Place of Publication |
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Editor |
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Original Title |
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Edition |
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ISBN |
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Expedition |
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Conference |
WORMS |
| Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRF2018 |
Serial |
3223 |
| Permanent link to this record |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
Type |
Conference Article |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
Abbreviated Journal |
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| Volume |
13639 |
Issue |
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Pages |
171-184 |
| Keywords |
Object detection; Optical music recognition; Graph neural network |
| Abstract |
During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
| Address |
December 04 – 07, 2022; Hyderabad, India |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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LNCS |
| Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICFHR |
| Notes |
DAG; 600.162; 600.140; 602.230 |
Approved |
no |
| Call Number |
Admin @ si @ BRF2022b |
Serial |
3740 |
| Permanent link to this record |
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| Author |
Arnau Baro; Jialuo Chen; Alicia Fornes; Beata Megyesi |
| Title |
Towards a generic unsupervised method for transcription of encoded manuscripts |
Type |
Conference Article |
| Year |
2019 |
Publication |
3rd International Conference on Digital Access to Textual Cultural Heritage |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
73-78 |
| Keywords |
A. Baró, J. Chen, A. Fornés, B. Megyesi. |
| Abstract |
Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods. |
| Address |
Brussels; May 2019 |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
DATeCH |
| Notes |
DAG; 600.097; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BCF2019 |
Serial |
3276 |
| Permanent link to this record |
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| Author |
Arnau Baro; Carles Badal; Pau Torras; Alicia Fornes |
| Title |
Handwritten Historical Music Recognition through Sequence-to-Sequence with Attention Mechanism |
Type |
Conference Article |
| Year |
2022 |
Publication |
3rd International Workshop on Reading Music Systems (WoRMS2021) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
55-59 |
| Keywords |
Optical Music Recognition; Digits; Image Classification |
| Abstract |
Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
| Address |
July 23, 2021, Alicante (Spain) |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
WoRMS |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ BBT2022 |
Serial |
3734 |
| Permanent link to this record |
