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Alicia Fornes; Asma Bensalah; Cristina Carmona_Duarte; Jialuo Chen; Miguel A. Ferrer; Andreas Fischer; Josep Llados; Cristina Martin; Eloy Opisso; Rejean Plamondon; Anna Scius-Bertrand; Josep Maria Tormos |
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The RPM3D Project: 3D Kinematics for Remote Patient Monitoring |
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Conference Article |
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2022 |
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Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
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13424 |
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217-226 |
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Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics |
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This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute (https://www.guttmann.com/en/) (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases. |
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June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ FBC2022 |
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3739 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
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Conference Article |
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2022 |
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Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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171-184 |
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Object detection; Optical music recognition; Graph neural network |
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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. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.162; 600.140; 602.230 |
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Admin @ si @ BRF2022b |
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3740 |
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Carlos Boned Riera; Oriol Ramos Terrades |
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Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
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2022 |
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26th International Conference on Pattern Recognition |
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2186-2191 |
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Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
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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. |
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Montreal; Quebec; Canada; August 2022 |
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DAG; 600.121; 600.162 |
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Admin @ si @ BoR2022 |
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3741 |
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Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
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Title |
MUST-VQA: MUltilingual Scene-text VQA |
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Conference Article |
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2022 |
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Proceedings European Conference on Computer Vision Workshops |
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13804 |
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345–358 |
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Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
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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. |
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Tel-Aviv; Israel; October 2022 |
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ECCVW |
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DAG; 302.105; 600.155; 611.002 |
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Admin @ si @ VBM2022 |
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3770 |
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Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
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Title |
Out-of-Vocabulary Challenge Report |
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Conference Article |
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2022 |
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Proceedings European Conference on Computer Vision Workshops |
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13804 |
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359–375 |
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This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
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Tel-Aviv; Israel; October 2022 |
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DAG; 600.155; 302.105; 611.002 |
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Admin @ si @ GMB2022 |
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3771 |
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Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
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Title |
Read While You Drive-Multilingual Text Tracking on the Road |
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Conference Article |
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2022 |
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15th IAPR International workshop on document analysis systems |
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13237 |
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756–770 |
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Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild. |
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La Rochelle; France; May 2022 |
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978-3-031-06554-5 |
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DAS |
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DAG; 600.155; 611.022; 611.004 |
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Admin @ si @ GTR2022 |
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3783 |
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Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
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TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
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2022 |
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26th International Conference on Pattern Recognition |
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Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
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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 |
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Montreal; Quebec; Canada; August 2022 |
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DAG; |
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Admin @ si @ BSA2022 |
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3788 |
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Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai |
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Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks |
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Conference Article |
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2022 |
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17th European Conference on Computer Vision Workshops |
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13804 |
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329–344 |
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Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. |
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978-3-031-25068-2 |
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ECCV-TiE |
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DAG; 600.162; 600.140; 110.312 |
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Admin @ si @ GBC2022 |
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3795 |
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Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados |
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Classification of aesthetic natural scene images using statistical and semantic features |
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2023 |
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Multimedia Tools and Applications |
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MTAP |
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82 |
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9 |
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13507-13532 |
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Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. |
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Admin @ si @ BSP2023 |
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3873 |
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Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados |
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I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation |
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2023 |
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21st International Graphonomics Conference |
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136–148 |
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During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. |
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Evora; Portugal; October 2023 |
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Admin @ si @ BPG2023 |
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3838 |
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