| |
Records |
Links |
|
Author |
Arnau Baro |
|
| |
Title |
Reading Music Systems: From Deep Optical Music Recognition to Contextual Methods |
Type |
Book Whole |
| |
Year  |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
The transcription of sheet music into some machine-readable format can be carried out manually. However, the complexity of music notation inevitably leads to burdensome software for music score editing, which makes the whole process
very time-consuming and prone to errors. Consequently, automatic transcription
systems for musical documents represent interesting tools.
Document analysis is the subject that deals with the extraction and processing
of documents through image and pattern recognition. It is a branch of computer
vision. Taking music scores as source, the field devoted to address this task is
known as Optical Music Recognition (OMR). Typically, an OMR system takes an
image of a music score and automatically extracts its content into some symbolic
structure such as MEI or MusicXML.
In this dissertation, we have investigated different methods for recognizing a
single staff section (e.g. scores for violin, flute, etc.), much in the same way as most text recognition research focuses on recognizing words appearing in a given line image. These methods are based in two different methodologies. On the one hand, we present two methods based on Recurrent Neural Networks, in particular, the
Long Short-Term Memory Neural Network. On the other hand, a method based on Sequence to Sequence models is detailed.
Music context is needed to improve the OMR results, just like language models
and dictionaries help in handwriting recognition. For example, syntactical rules
and grammars could be easily defined to cope with the ambiguities in the rhythm.
In music theory, for example, the time signature defines the amount of beats per
bar unit. Thus, in the second part of this dissertation, different methodologies
have been investigated to improve the OMR recognition. We have explored three
different methods: (a) a graphic tree-structure representation, Dendrograms, that
joins, at each level, its primitives following a set of rules, (b) the incorporation of Language Models to model the probability of a sequence of tokens, and (c) graph neural networks to analyze the music scores to avoid meaningless relationships between music primitives.
Finally, to train all these methodologies, and given the method-specificity of
the datasets in the literature, we have created four different music datasets. Two of them are synthetic with a modern or old handwritten appearance, whereas the
other two are real handwritten scores, being one of them modern and the other
old. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
| |
Publisher |
IMPRIMA |
Place of Publication |
|
Editor |
Alicia Fornes |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-84-124793-8-6 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ Bar2022 |
Serial |
3754 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
|
| |
Volume |
|
Issue |
|
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) |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
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 |
| |
|
| |
|
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 |
|
|
| |
Volume |
13639 |
Issue |
|
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 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICFHR |
|
| |
Notes |
DAG; 600.162; 600.140; 602.230 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRF2022b |
Serial |
3740 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados |
|
| |
Title |
Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
Abbreviated Journal |
|
|
| |
Volume |
13424 |
Issue |
|
Pages |
336-348 |
|
| |
Keywords |
Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk |
|
| |
Abstract |
Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. |
|
| |
Address |
June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
IGS |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BFC2022 |
Serial |
3738 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
|
| |
Title |
TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
|
| |
Abstract |
Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
|
| |
Address |
Montreal; Quebec; Canada; August 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
DAG; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BSA2022 |
Serial |
3788 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Carlos Boned Riera; Oriol Ramos Terrades |
|
| |
Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2186-2191 |
|
| |
Keywords |
Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
|
| |
Abstract |
Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
|
| |
Address |
Montreal; Quebec; Canada; August 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
DAG; 600.121; 600.162 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BoR2022 |
Serial |
3741 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
|
| |
Title |
MUST-VQA: MUltilingual Scene-text VQA |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
13804 |
Issue |
|
Pages |
345–358 |
|
| |
Keywords |
Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
|
| |
Abstract |
In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. |
|
| |
Address |
Tel-Aviv; Israel; October 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ECCVW |
|
| |
Notes |
DAG; 302.105; 600.155; 611.002 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ VBM2022 |
Serial |
3770 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
|
| |
Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
153-158 |
|
| |
Keywords |
transcription of ciphers; hand-written text recognition of symbols; graphic signs |
|
| |
Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
|
| |
Address |
Amsterdam, Netherlands, June 20-22, 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
HystoCrypt |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MBS2022 |
Serial |
3731 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
|
| |
Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
Abbreviated Journal |
|
|
| |
Volume |
13639 |
Issue |
|
Pages |
3-12 |
|
| |
Keywords |
N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
|
| |
Abstract |
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
|
| |
Address |
December 04 – 07, 2022; Hyderabad, India |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICFHR |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GBS2022 |
Serial |
3733 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre |
|
| |
Title |
The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data |
Type |
Journal |
| |
Year |
2022 |
Publication |
Historical Life Course Studies |
Abbreviated Journal |
HLCS |
|
| |
Volume |
12 |
Issue |
|
Pages |
99-132 |
|
| |
Keywords |
Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences |
|
| |
Abstract |
The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography. |
|
| |
Address |
June 23, 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PFR2022 |
Serial |
3737 |
|
| Permanent link to this record |
