| Records |
Author  |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
| Title |
SemiDocSeg: Harnessing Semi-Supervised Learning for Document Layout Analysis |
Type |
Journal Article |
| Year |
2024 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
| Volume |
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Issue |
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Pages |
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| Keywords |
Document layout analysis; Semi-supervised learning; Co-Occurrence matrix; Instance segmentation; Swin transformer |
| Abstract |
Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach. |
| Address |
June 2024 |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ BBL2024a |
Serial |
4001 |
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| Author |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
| Title |
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation |
Type |
Miscellaneous |
| Year |
2024 |
Publication |
Arxiv |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ BBL2024b |
Serial |
4023 |
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| Author |
Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
| Title |
TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
Type |
Conference Article |
| Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
| Abstract |
Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
| Address |
Montreal; Quebec; Canada; August 2022 |
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Conference |
ICPR |
| Notes |
DAG; |
Approved |
no |
| Call Number |
Admin @ si @ BSA2022 |
Serial |
3788 |
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| Author |
Axel Barroso-Laguna; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
| Title |
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters |
Type |
Conference Article |
| Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
5835-5843 |
| Keywords |
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| Abstract |
We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity. |
| Address |
Seul; Corea; October 2019 |
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Conference |
ICCV |
| Notes |
MSIAU; 600.122 |
Approved |
no |
| Call Number |
Admin @ si @ BRP2019 |
Serial |
3290 |
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| Author |
Aura Hernandez-Sabate; Petia Radeva; Antonio Tovar; Debora Gil |
| Title |
Vessel structures alignment by spectral analysis of ivus sequences |
Type |
Conference Article |
| Year |
2006 |
Publication |
Proc. of CVII, MICCAI Workshop |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
39-36 |
| Keywords |
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| Abstract |
Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS. |
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Thesis |
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Place of Publication |
Copenhaguen (Denmark), |
Editor |
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Original Title |
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Series Title |
1st International Wokshop on Computer Vision for Intravascular and Intracardiac Imaging (CVII’06) |
Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
IAM; MILAB |
Approved |
no |
| Call Number |
IAM @ iam @ HRT2006 |
Serial |
1552 |
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| Author |
Aura Hernandez-Sabate; Monica Mitiko; Sergio Shiguemi; Debora Gil |
| Title |
A validation protocol for assessing cardiac phase retrieval in IntraVascular UltraSound |
Type |
Conference Article |
| Year |
2010 |
Publication |
Computing in Cardiology |
Abbreviated Journal |
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| Volume |
37 |
Issue |
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Pages |
899-902 |
| Keywords |
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| Abstract |
A good reliable approach to cardiac triggering is of utmost importance in obtaining accurate quantitative results of atherosclerotic plaque burden from the analysis of IntraVascular UltraSound. Although, in the last years, there has been an increase in research of methods for retrospective gating, there is no general consensus in a validation protocol. Many methods are based on quality assessment of longitudinal cuts appearance and those reporting quantitative numbers do not follow a standard protocol. Such heterogeneity in validation protocols makes faithful comparison across methods a difficult task. We propose a validation protocol based on the variability of the retrieved cardiac phase and explore the capability of several quality measures for quantifying such variability. An ideal detector, suitable for its application in clinical practice, should produce stable phases. That is, it should always sample the same cardiac cycle fraction. In this context, one should measure the variability (variance) of a candidate sampling with respect a ground truth (reference) sampling, since the variance would indicate how spread we are aiming a target. In order to quantify the deviation between the sampling and the ground truth, we have considered two quality scores reported in the literature: signed distance to the closest reference sample and distance to the right of each reference sample. We have also considered the residuals of the regression line of reference against candidate sampling. The performance of the measures has been explored on a set of synthetic samplings covering different cardiac cycle fractions and variabilities. From our simulations, we conclude that the metrics related to distances are sensitive to the shift considered while the residuals are robust against fraction and variabilities as far as one can establish a pair-wise correspondence between candidate and reference. We will further investigate the impact of false positive and negative detections in experimental data. |
| Address |
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Thesis |
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| Publisher |
IEEE |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0276-6547 |
ISBN |
978-1-4244-7318-2 |
Medium |
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| Area |
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Expedition |
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Conference |
CINC |
| Notes |
IAM; |
Approved |
no |
| Call Number |
IAM @ iam @ HSM2010 |
Serial |
1551 |
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| Author |
Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin |
| Title |
Mathematics learning opportunities when playing a Tower Defense Game |
Type |
Journal |
| Year |
2015 |
Publication |
International Journal of Serious Games |
Abbreviated Journal |
IJSG |
| Volume |
2 |
Issue |
4 |
Pages |
57-71 |
| Keywords |
Tower Defense game; learning opportunities; mathematics; problem solving; game design |
| Abstract |
A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes. |
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Conference |
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| Notes |
ADAS; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @ HJG2015 |
Serial |
2730 |
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| Author |
Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez |
| Title |
Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem |
Type |
Journal |
| Year |
2020 |
Publication |
Mathematics |
Abbreviated Journal |
MATH |
| Volume |
20 |
Issue |
8(9) |
Pages |
1595 |
| Keywords |
STEM education; Project-based learning; Coding; software tool |
| Abstract |
In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
| Address |
September 2020 |
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| Notes |
IAM; ISE |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
3722 |
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| Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
| Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
Type |
Conference Article |
| Year |
2016 |
Publication |
5th International Conference Games and Learning Alliance |
Abbreviated Journal |
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| Volume |
10056 |
Issue |
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Pages |
50-59 |
| Keywords |
Simulation environment; Automated Driving; Driver-Vehicle interaction |
| Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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LNCS |
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Conference |
GALA |
| Notes |
ADAS;IAM; |
Approved |
no |
| Call Number |
HAC2016 |
Serial |
2864 |
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| Author |
Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil |
| Title |
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals |
Type |
Journal Article |
| Year |
2022 |
Publication |
Applied Sciences |
Abbreviated Journal |
APPLSCI |
| Volume |
12 |
Issue |
5 |
Pages |
2298 |
| Keywords |
Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion |
| Abstract |
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. |
| Address |
February 2022 |
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| Notes |
IAM; ADAS; 600.139; 600.145; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ HYF2022 |
Serial |
3720 |
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| Author |
Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil |
| Title |
EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment |
Type |
Journal Article |
| Year |
2024 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
24 |
Issue |
4 |
Pages |
1174 |
| Keywords |
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| Abstract |
High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. |
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| Notes |
IAM |
Approved |
no |
| Call Number |
Admin @ si @ HYF2024 |
Serial |
4019 |
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| Author |
Aura Hernandez-Sabate; Debora Gil;Eduard Fernandez-Nofrerias;Petia Radeva; Enric Marti |
| Title |
Approaching Artery Rigid Dynamics in IVUS |
Type |
Journal Article |
| Year |
2009 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
| Volume |
28 |
Issue |
11 |
Pages |
1670-1680 |
| Keywords |
Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation. |
| Abstract |
Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases. |
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Edition |
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| ISSN |
0278-0062 |
ISBN |
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Medium |
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Conference |
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| Notes |
IAM; MILAB |
Approved |
no |
| Call Number |
IAM @ iam @ HGF2009 |
Serial |
1545 |
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| Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva; E.N.Nofrerias |
| Title |
Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images |
Type |
Conference Article |
| Year |
2004 |
Publication |
Proc. Computers in Cardiology |
Abbreviated Journal |
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| Volume |
31 |
Issue |
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Pages |
229-232 |
| Keywords |
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| Abstract |
The adventitia layer appears as a weak edge in IVUS images with a non-uniform grey level, which difficulties its detection. In order to enhance edges, we apply an anisotropic filter that homogenizes the grey level along the image significant structures (ridges, valleys and edges). A standard edge detector applied to the filtered image yields a set of candidate points prone to be unconnected. The final model is obtained by interpolating the former line segments along the tangent direction to the level curves of the filtered image with an anisotropic contour closing technique based on functional extension principles |
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Thesis |
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Place of Publication |
Chicago (USA) |
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| Notes |
IAM; MILAB |
Approved |
no |
| Call Number |
IAM @ iam @ HGR2004 |
Serial |
1555 |
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| Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
| Title |
A Deterministic-Statistical Strategy for Adventitia Segmentation in IVUS images |
Type |
Report |
| Year |
2005 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
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Issue |
89 |
Pages |
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| Keywords |
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| Abstract |
A useful tool for some specific studies in cardiac disease diagnosis is vessel plaque assessment by analysis of IVUS sequences. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts and blurred signal response due to ultrasound physical properties troubles automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of inter-observer variability regardless of plaque nature, vessel geometry and incomplete vessel borders. |
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IAM @ iam @ HGR2005a |
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1548 |
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| Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
| Title |
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging |
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Conference Article |
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2005 |
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Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
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67-74 |
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classification; vessel border modelling; IVUS |
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IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability. |
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IOS Press |
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Amsterdam, The Netherlands |
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IAM @ iam @ HGR2005c |
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1549 |
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