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Martha Mackay; Fernando Alonso; Pere Salamero; Xavier Baro; Jordi Gonzalez; Sergio Escalera |
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Care and caring: future proofing the new demographics |
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Conference Article |
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2015 |
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6th International Carers Conference |
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With an ageing population, the issue of care provision is becoming increasingly important. The simple aspiration of the majority of older people is to live safely and well at home. Housing will be part of health & care integration in the following years and decades. A higher proportion of people will have to rely on informal care through family, friends, neighbors and others who
provide care to an older person in need of assistance (around 80% of care across the EU). They do not usually have a formal status and are usually unpaid. We need to ensure that all disabled or chronically ill people can get the help they need without overburdening their families.
The physical and emotional stress of carers is one of the dangers that this dependency can bring. To prevent carers burnout it is necessary to provide new solutions that are affordable and user friendly for the families and caregivers. |
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Gothenburg; Sweden; September 2015 |
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CARERS |
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HuPBA; ISE; 600.078;MV |
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Admin @ si @ MAS2015b |
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2678 |
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Author |
Ricardo Toledo |
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Cardiac workstation and dynamic model to assist in coronary tree analysis. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Petia Radeva;JuanJose Villanueva |
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ADAS |
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Admin @ si @ Tol2001 |
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166 |
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Author |
Fernando Vilariño; Petia Radeva |
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Title |
Cardiac Segmentation with Discriminant Active Contours |
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2003 |
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211–217 |
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Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods. |
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Palma de Mallorca |
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IOS Press |
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CCIA |
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MV;MILAB;SIAI |
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BCNPCL @ bcnpcl @ ViR2003; IAM @ iam @ VRa2003 |
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426 |
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Joel Barajas; Karla Lizbeth Caballero; Petia Radeva |
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Title |
Cardiac Phase Extraction in IVUS Sequences Using 1-D Gabor Filters |
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2007 |
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Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE |
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343–36 |
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Lyon (France) |
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MILAB |
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BCNPCL @ bcnpcl @ BCR2007 |
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924 |
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Oriol Rodriguez-Leor; R. Hemetsberger; Francesco Ciompi; E Fernandez-Nofrerias; Angel Serrano; M. Bernet; Petia Radeva; F. Mauri; A. Bayes |
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Caracteritzacio automatica de la placa mitjançant analisis del espectre de radiofreqüencia en estudi de ecografia intracoronaria: resultat de la fusio de dades invivo i exvivo |
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Conference Article |
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2010 |
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22nd Congres Societat Catalana de Cardiologia, |
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131 |
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Barcelona (Spain) |
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MILAB |
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BCNPCL @ bcnpcl @ RHC2010 |
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1368 |
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Author |
Oriol Rodriguez-Leor; Debora Gil; Eduard Fernandez-Nofrerias; H. Tizon; S. Montserrat; Vicente del Valle; Josefina Mauri |
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Title |
Caracterització de la Perfusió Miocàrdica mitjançant anàlisi estadístic de l espectre en l angiografia de contrast |
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Conference Article |
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2007 |
Publication |
XIX Congrés de la Societat Catalana de Cardiologia de Barcelona |
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130 |
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La valoració de la integritat de la microcirculació coronària aporta informació pronòstica en pacients amb infart agut de miocardi en els que es realitza angioplastia primària. Aquesta valoració és subjectiva i presenta una important variabilitat si no es duta a terme per personal experimentat. Presentem una eina d’anàlisi d’imatge que permet fer una valoració de la microcirculació coronària a partir de seqüències d’angiografia. Hem analitzat les variacions locals en el nivell de gris de la imatge durant la seqüència angiogràfica. Hem identificat els principals fenòmens observats (respiració, batec cardíac, tinció arterial, tinció miocàrdica i soroll radiològic) mitjançant un anàlisi estadístic de l’espectre de Fourier de l’evolució al llarg del temps de la mitja local. Aquest mateix anàlisis permet determinat la influència de cadascun d’ells en la extracció del patró de tinció i selecciona la respiració com el fenomen que més distorsiona el patró de tinció original. Els descriptors proposats s’obtenen fora del rang espectral respiratori. Hem testat la seva capacitat per a detectar els tres fenòmens principals (tinció miocàrdica (MS), tinció arterial (AS) i soroll (NS)) independentment de la respiració. La capacitat de discriminació dels descriptors ha estat valorada mitjançant un mètode de crossvalidation en 30 seqüències d’angiografia. Els descriptors emprats permeten caracteritzar la tinció miocàrdica amb una alta eficiència i fiabilitat. A més no hi ha diferències significatives en l’anàlisi de les seqüències obtingudes amb el pacient respirant amb normalitat o en apnea |
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Barcelona (Spain) |
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IAM |
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IAM @ iam @ RGF2007 |
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1639 |
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Author |
Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Xavier Carrillo; R. Hemetsberger; Petia Radeva; Josefina Mauri; A. Bayes |
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Canvis de volum a la arteria radial despres de la administracio de dos tractaments vasodilatadors. Avaluacio mitjançant ecografia intravascular |
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Conference Article |
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2010 |
Publication |
22nd Congres Societat Catalana de Cardiologia, |
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179 |
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Barcelona (Spain) |
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MILAB |
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BCNPCL @ bcnpcl @ SFC2010a |
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1367 |
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Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol |
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Title |
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture |
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Journal Article |
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2021 |
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Pattern Recognition |
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PR |
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112 |
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107790 |
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Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging
problem. The main challenge faced when training a language model is to
deal with the language model corpus which is usually different to the one
used for training the handwritten word recognition system. Thus, the bias
between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this
work, we introduce Candidate Fusion, a novel way to integrate an external
language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to
the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two
improvements. On the one hand, the sequence-to-sequence recognizer has
the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided
by the language model. On the other hand, the external language model
has the ability to adapt itself to the training corpus and even learn the
most commonly errors produced from the recognizer. Finally, by conducting
comprehensive experiments, the Candidate Fusion proves to outperform the
state-of-the-art language models for handwritten word recognition tasks. |
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DAG; 600.140; 601.302; 601.312; 600.121 |
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Admin @ si @ KRV2021 |
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3343 |
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Author |
Adarsh Tiwari; Sanket Biswas; Josep Llados |
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Title |
Can Pre-trained Language Models Help in Understanding Handwritten Symbols? |
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Conference Article |
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2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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14193 |
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199–211 |
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The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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Admin @ si @ TBL2023 |
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3908 |
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Author |
Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? |
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Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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260-267 |
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Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language. |
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Sydney; Australia; September 2019 |
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DAG; 600.129; 600.121 |
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Admin @ si @ RVK2019 |
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3337 |
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Author |
Petia Radeva |
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Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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4 |
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Barcelona; October 2016 |
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Admin @ si @ Rad2016 |
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2832 |
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Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu |
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Can biological solutions help computers detect symmetry? |
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2017 |
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40th European Conference on Visual Perception |
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Berlin; Germany; August 2017 |
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ECVP |
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NEUROBIT |
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Admin @ si @ APE2017 |
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2995 |
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Pedro Herruzo; Marc Bolaños; Petia Radeva |
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Title |
Can a CNN Recognize Catalan Diet? |
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2016 |
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AIP Conference Proceedings |
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1773 |
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CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
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MILAB |
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no |
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Admin @ si @ HBR2016 |
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2837 |
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Author |
Adria Rico; Alicia Fornes |
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Camera-based Optical Music Recognition using a Convolutional Neural Network |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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27-28 |
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optical music recognition; document analysis; convolutional neural network; deep learning |
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Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results |
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GREC |
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DAG;600.097; 600.121 |
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Admin @ si @ RiF2017 |
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3059 |
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Marçal Rusiñol; Josep Llados; Philippe Dosch |
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Camera-Based Graphical Symbol Detection |
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2007 |
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9th IEEE International Conference on Document Analysis and Recognition |
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2 |
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884–888 |
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Curitiba (Brazil) |
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ICDAR |
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DAG |
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DAG @ dag @ RLD2007 |
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848 |
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