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Author | Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca | ||||
Title | Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification | Type | Journal Article | ||
Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
Volume | Issue | Pages | 1-11 | ||
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Abstract ![]() |
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database. | ||||
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Notes | ISE; 600.119; 600.098 | Approved | no | ||
Call Number | Admin @ si @ RCG2017a | Serial | 2926 | ||
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Author | Ramin Irani; Kamal Nasrollahi; Chris Bahnsen; D.H. Lundtoft; Thomas B. Moeslund; Marc O. Simon; Ciprian Corneanu; Sergio Escalera; Tanja L. Pedersen; Maria-Louise Klitgaard; Laura Petrini | ||||
Title | Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition | Type | Conference Article | ||
Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 88-95 | ||
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Abstract ![]() |
Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions. |
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Address | Boston; EEUU; June 2015 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ INB2015 | Serial | 2654 | ||
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Author | Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi | ||||
Title | Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures | Type | Conference Article | ||
Year | 2018 | Publication | 13th IEEE Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 250 - 257 | ||
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Abstract ![]() |
Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate. | ||||
Address | Xian; China; May 2018 | ||||
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Area | Expedition | Conference | FG | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ HBN2018 | Serial | 3117 | ||
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Author | Miguel Oliveira; Victor Santos; Angel Sappa | ||||
Title | Multimodal Inverse Perspective Mapping | Type | Journal Article | ||
Year | 2015 | Publication | Information Fusion | Abbreviated Journal | IF |
Volume | 24 | Issue | Pages | 108–121 | |
Keywords | Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles | ||||
Abstract ![]() |
Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. | ||||
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Notes | ADAS; 600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ OSS2015c | Serial | 2532 | ||
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Author | Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Review on Emotion Recognition Databases | Type | Book Chapter | ||
Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | emotion; computer vision; databases | ||||
Abstract ![]() |
Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. | ||||
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ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | HUPBA; 602.133 | Approved | no | ||
Call Number | Admin @ si @ HRL2018 | Serial | 3212 | ||
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Author | Cristhian Aguilera | ||||
Title | Local feature description in cross-spectral imagery | Type | Book Whole | ||
Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract ![]() |
Over the last few years, the number of consumer computer vision applications has increased dramatically. Today, computer vision solutions can be found in video game consoles, smartphone applications, driving assistance – just to name a few. Ideally, we require the performance of those applications, particularly those that are safety critical to remain constant under any external environment factors, such as changes in illumination or weather conditions. However, this is not always possible or very difficult to obtain by only using visible imagery, due to the inherent limitations of the images from that spectral band. For that reason, the use of images from different or multiple spectral bands is becoming more appealing.
The aforementioned possible advantages of using images from multiples spectral bands on various vision applications make multi-spectral image processing a relevant topic for research and development. Like in visible image processing, multi-spectral image processing needs tools and algorithms to handle information from various spectral bands. Furthermore, traditional tools such as local feature detection, which is the basis of many vision tasks such as visual odometry, image registration, or structure from motion, must be adjusted or reformulated to operate under new conditions. Traditional feature detection, description, and matching methods tend to underperform in multi-spectral settings, in comparison to mono-spectral settings, due to the natural differences between each spectral band. The work in this thesis is focused on the local feature description problem when cross-spectral images are considered. In this context, this dissertation has three main contributions. Firstly, the work starts by proposing the usage of a combination of frequency and spatial information, in a multi-scale scheme, as feature description. Evaluations of this proposal, based on classical hand-made feature descriptors, and comparisons with state of the art cross-spectral approaches help to find and understand limitations of such strategy. Secondly, different convolutional neural network (CNN) based architectures are evaluated when used to describe cross-spectral image patches. Results showed that CNN-based methods, designed to work with visible monocular images, could be successfully applied to the description of images from two different spectral bands, with just minor modifications. In this framework, a novel CNN-based network model, specifically intended to describe image patches from two different spectral bands, is proposed. This network, referred to as Q-Net, outperforms state of the art in the cross-spectral domain, including both previous hand-made solutions as well as L2 CNN-based architectures. The third contribution of this dissertation is in the cross-spectral feature description application domain. The multispectral odometry problem is tackled showing a real application of cross-spectral descriptors In addition to the three main contributions mentioned above, in this dissertation, two different multi-spectral datasets are generated and shared with the community to be used as benchmarks for further studies. |
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Address | October 2017 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa | |
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ISSN | ISBN | 978-84-945373-6-3 | Medium | ||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Agu2017 | Serial | 3020 | ||
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Author | Oriol Ramos Terrades; Albert Berenguel; Debora Gil | ||||
Title | A flexible outlier detector based on a topology given by graph communities | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract ![]() |
Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. | ||||
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Notes | IAM; DAG; 600.139; 600.145; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RBG2020 | Serial | 3475 | ||
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Author | Oriol Ramos Terrades; Albert Berenguel; Debora Gil | ||||
Title | A Flexible Outlier Detector Based on a Topology Given by Graph Communities | Type | Journal Article | ||
Year | 2022 | Publication | Big Data Research | Abbreviated Journal | BDR |
Volume | 29 | Issue | Pages | 100332 | |
Keywords | Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors | ||||
Abstract ![]() |
Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
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Address | August 28, 2022 | ||||
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Notes | DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 | Approved | no | ||
Call Number | Admin @ si @ RBG2022a | Serial | 3718 | ||
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Author | Zhijie Fang; Antonio Lopez | ||||
Title | Is the Pedestrian going to Cross? Answering by 2D Pose Estimation | Type | Conference Article | ||
Year | 2018 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
Volume | Issue | Pages | 1271 - 1276 | ||
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Abstract ![]() |
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. | ||||
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Area | Expedition | Conference | IV | ||
Notes | ADAS; 600.124; 600.116; 600.118 | Approved | no | ||
Call Number | Admin @ si @ FaL2018 | Serial | 3181 | ||
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Author | Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli | ||||
Title | Seeing and Hearing Egocentric Actions: How Much Can We Learn? | Type | Conference Article | ||
Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 4470-4480 | ||
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Abstract ![]() |
Our interaction with the world is an inherently multimodal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a 5.18% improvement over the state of the art on verb classification. | ||||
Address | Seul; Korea; October 2019 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ CLR2019b | Serial | 3385 | ||
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Author | Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez | ||||
Title | 3d Pedestrian Detection via Random Forest | Type | Miscellaneous | ||
Year | 2014 | Publication | European Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 231-238 | ||
Keywords | Pedestrian Detection | ||||
Abstract ![]() |
Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications. |
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Address | Zurich; suiza; September 2014 | ||||
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Area | Expedition | Conference | ECCV-Demo | ||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ VRR2014 | Serial | 2570 | ||
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Author | Mariella Dimiccoli; Benoît Girard; Alain Berthoz; Daniel Bennequin | ||||
Title | Striola Magica: a functional explanation of otolith organs | Type | Journal Article | ||
Year | 2013 | Publication | Journal of Computational Neuroscience | Abbreviated Journal | JCN |
Volume | 35 | Issue | 2 | Pages | 125-154 |
Keywords | Otolith organs ;Striola; Vestibular pathway | ||||
Abstract ![]() |
Otolith end organs of vertebrates sense linear accelerations of the head and gravitation. The hair cells on their epithelia are responsible for transduction. In mammals, the striola, parallel to the line where hair cells reverse their polarization, is a narrow region centered on a curve with curvature and torsion. It has been shown that the striolar region is functionally different from the rest, being involved in a phasic vestibular pathway. We propose a mathematical and computational model that explains the necessity of this amazing geometry for the striola to be able to carry out its function. Our hypothesis, related to the biophysics of the hair cells and to the physiology of their afferent neurons, is that striolar afferents collect information from several type I hair cells to detect the jerk in a large domain of acceleration directions. This predicts a mean number of two calyces for afferent neurons, as measured in rodents. The domain of acceleration directions sensed by our striolar model is compatible with the experimental results obtained on monkeys considering all afferents. Therefore, the main result of our study is that phasic and tonic vestibular afferents cover the same geometrical fields, but at different dynamical and frequency domains. | ||||
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Publisher | Springer US | Place of Publication | Editor | ||
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ISSN | 1573-6873. 2013 | ISBN | Medium | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @DBG2013 | Serial | 2787 | ||
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Author | Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez | ||||
Title | Computing the Testing Error Without a Testing Set | Type | Conference Article | ||
Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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Abstract ![]() |
Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach. |
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Address | Virtual CVPR | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CEM2020 | Serial | 3437 | ||
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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 | |
Volume | 11009 | Issue | 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. | ||||
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Publisher | Springer | Place of Publication | Editor | A. Fornes, B. Lamiroy | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
Area | Expedition | Conference | GREC | ||
Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRC2018 | Serial | 3227 | ||
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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 | |
Volume | Issue | 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 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRC2017 | Serial | 3056 | ||
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