| Records |
| Author |
Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
| Title |
A benchmark for the evaluation of computational methods for bronchoscopic navigation |
Type |
Journal Article |
| Year |
2022 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCARS |
| Volume |
17 |
Issue |
1 |
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IAM |
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no |
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Admin @ si @ BSC2022 |
Serial |
3832 |
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| Author |
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
| Title |
EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Thoracic Oncology |
Abbreviated Journal |
JTO |
| Volume |
17 |
Issue |
9 |
Pages |
S182 |
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IAM |
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no |
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Admin @ si @ RBG2022b |
Serial |
3834 |
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| Author |
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
| Title |
Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. |
Type |
Journal Article |
| Year |
2022 |
Publication |
European Respiratory Journal |
Abbreviated Journal |
ERJ |
| Volume |
60 |
Issue |
66 |
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IAM |
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no |
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Admin @ si @ RBG2022c |
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3835 |
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| Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
| Title |
Hierarchical multimodal transformers for Multipage DocVQA |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
144 |
Issue |
109834 |
Pages |
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| Abstract |
Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure. |
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DAG |
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no |
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Admin @ si @ TKV2023 |
Serial |
3836 |
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| Author |
Bhalaji Nagarajan; Marc Bolaños; Eduardo Aguilar; Petia Radeva |
| Title |
Deep ensemble-based hard sample mining for food recognition |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
| Volume |
95 |
Issue |
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Pages |
103905 |
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| Abstract |
Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics. |
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MILAB |
Approved |
no |
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Admin @ si @ NBA2023 |
Serial |
3844 |
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| Author |
Cristhian A. Aguilera-Carrasco; Luis Felipe Gonzalez-Böhme; Francisco Valdes; Francisco Javier Quitral Zapata; Bogdan Raducanu |
| Title |
A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
100975 - 100985 |
| Keywords |
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| Abstract |
This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards. |
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LAMP |
Approved |
no |
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Admin @ si @ AGV2023 |
Serial |
3969 |
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| Author |
Roger Max Calle Quispe; Maya Aghaei Gavari; Eduardo Aguilar Torres |
| Title |
Towards real-time accurate safety helmets detection through a deep learning-based method |
Type |
Journal |
| Year |
2023 |
Publication |
Ingeniare. Revista chilena de ingenieria |
Abbreviated Journal |
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| Volume |
31 |
Issue |
12 |
Pages |
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Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.
These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability. |
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MILAB |
Approved |
no |
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Admin @ si @ CAA2023 |
Serial |
3846 |
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| Author |
G. Gasbarri; Matias Bilkis; E. Roda Salichs; J. Calsamiglia |
| Title |
Sequential hypothesis testing for continuously-monitored quantum systems |
Type |
Journal Article |
| Year |
2024 |
Publication |
Quantum |
Abbreviated Journal |
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| Volume |
8 |
Issue |
1289 |
Pages |
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We consider a quantum system that is being continuously monitored, giving rise to a measurement signal. From such a stream of data, information needs to be inferred about the underlying system's dynamics. Here we focus on hypothesis testing problems and put forward the usage of sequential strategies where the signal is analyzed in real time, allowing the experiment to be concluded as soon as the underlying hypothesis can be identified with a certified prescribed success probability. We analyze the performance of sequential tests by studying the stopping-time behavior, showing a considerable advantage over currently-used strategies based on a fixed predetermined measurement time. |
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xxxx |
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no |
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Admin @ si @ GBR2024 |
Serial |
3847 |
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| Author |
Mohamed Ali Souibgui; Sanket Biswas; Andres Mafla; Ali Furkan Biten; Alicia Fornes; Yousri Kessentini; Josep Llados; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 37th AAAI Conference on Artificial Intelligence |
Abbreviated Journal |
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| Volume |
37 |
Issue |
2 |
Pages |
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| Keywords |
Representation Learning for Vision; CV Applications; CV Language and Vision; ML Unsupervised; Self-Supervised Learning |
| Abstract |
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR |
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AAAI |
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DAG |
Approved |
no |
| Call Number |
Admin @ si @ SBM2023 |
Serial |
3848 |
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| Author |
Mohamed Ali Souibgui; Pau Torras; Jialuo Chen; Alicia Fornes |
| Title |
An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts |
Type |
Conference Article |
| Year |
2023 |
Publication |
7th International Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
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Issue |
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Pages |
7-12 |
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This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage. |
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HIP |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ STC2023 |
Serial |
3849 |
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| Author |
Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
| Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
Type |
Conference Article |
| Year |
2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
Abbreviated Journal |
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| Volume |
14193 |
Issue |
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Pages |
83-93 |
| Keywords |
Historical Manuscripts; Symbol Alignment |
| Abstract |
Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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LNCS |
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ICDAR |
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DAG |
Approved |
no |
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Admin @ si @ TSS2023 |
Serial |
3850 |
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| Author |
Marwa Dhiaf; Mohamed Ali Souibgui; Kai Wang; Yuyang Liu; Yousri Kessentini; Alicia Fornes; Ahmed Cheikh Rouhou |
| Title |
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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| Abstract |
Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available. |
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DAG |
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no |
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Admin @ si @ DSW2023 |
Serial |
3851 |
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| Author |
Jose Elias Yauri; M. Lagos; H. Vega-Huerta; P. de-la-Cruz; G.L.E Maquen-Niño; E. Condor-Tinoco |
| Title |
Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings |
Type |
Journal Article |
| Year |
2023 |
Publication |
International Journal of Advanced Computer Science and Applications |
Abbreviated Journal |
IJACSA |
| Volume |
14 |
Issue |
5 |
Pages |
1067-1074 |
| Keywords |
Epilepsy; epilepsy detection; EEG; EEG channel fusion; convolutional neural network; self-attention |
| Abstract |
According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches. |
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IAM |
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no |
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Admin @ si @ |
Serial |
3856 |
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| Author |
M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat |
| Title |
Implicit Learning of Scene Geometry From Poses for Global Localization |
Type |
Journal Article |
| Year |
2024 |
Publication |
IEEE Robotics and Automation Letters |
Abbreviated Journal |
ROBOTAUTOMLET |
| Volume |
9 |
Issue |
2 |
Pages |
955-962 |
| Keywords |
Localization; Localization and mapping; Deep learning for visual perception; Visual learning |
| Abstract |
Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets. |
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2377-3766 |
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ADAS |
Approved |
no |
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Admin @ si @ |
Serial |
3857 |
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| Author |
P. Canals; Simone Balocco; O. Diaz; J. Li; A. Garcia Tornel; M. Olive Gadea; M. Ribo |
| Title |
A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning |
Type |
Journal Article |
| Year |
2023 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
| Volume |
104 |
Issue |
102170 |
Pages |
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Artificial intelligence; Deep learning; Stroke; Thrombectomy; Vascular feature extraction; Vascular tortuosity |
| Abstract |
Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients. |
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Admin @ si @ CBD2023 |
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4005 |
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