| Records |
| 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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| Notes |
IAM |
Approved |
no |
| Call Number |
Admin @ si @ RBG2022c |
Serial |
3835 |
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| Author |
Daniela Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa |
| Title |
A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Manufacturing Systems |
Abbreviated Journal |
JMANUFSYST |
| Volume |
64 |
Issue |
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Pages |
497-507 |
| Keywords |
Calibration; Collaborative cell; Multi-modal; Multi-sensor |
| Abstract |
Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs. |
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| Publisher |
Science Direct |
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MSIAU; MACO |
Approved |
no |
| Call Number |
Admin @ si @ ROS2022 |
Serial |
3750 |
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| Author |
Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach |
| Title |
Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge |
Type |
Journal Article |
| Year |
2022 |
Publication |
Gastrointestinal Endoscopy |
Abbreviated Journal |
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| Volume |
96 |
Issue |
2 |
Pages |
370-372 |
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ISE |
Approved |
no |
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Admin @ si @ MMB2022 |
Serial |
3701 |
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| Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
| Title |
Bayesian deep learning for semantic segmentation of food images |
Type |
Journal Article |
| Year |
2022 |
Publication |
Computers and Electrical Engineering |
Abbreviated Journal |
CEE |
| Volume |
103 |
Issue |
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Pages |
108380 |
| Keywords |
Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
| Abstract |
Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
| Address |
October 2022 |
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Science Direct |
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| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ANR2022 |
Serial |
3763 |
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| Author |
S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes |
| Title |
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
123 |
Issue |
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Pages |
108370 |
| Keywords |
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| Abstract |
Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. |
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| Notes |
DAG; 600.124; 600.121; 602.230 |
Approved |
no |
| Call Number |
Admin @ si @ JSK2022 |
Serial |
3613 |
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| Author |
Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados |
| Title |
Table detection in business document images by message passing networks |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
127 |
Issue |
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Pages |
108641 |
| Keywords |
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| Abstract |
Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. |
| Address |
July 2022 |
| Corporate Author |
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| Publisher |
Elsevier |
Place of Publication |
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| Notes |
DAG; 600.162; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ RGR2022 |
Serial |
3729 |
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| Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
| Title |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
129 |
Issue |
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Pages |
108766 |
| Keywords |
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| Abstract |
The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
| Address |
Sept. 2022 |
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| Notes |
DAG; 600.121; 600.162 |
Approved |
no |
| Call Number |
Admin @ si @ KRR2022 |
Serial |
3556 |
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| Author |
Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund |
| Title |
Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification |
Type |
Journal Article |
| Year |
2022 |
Publication |
Automation in Construction |
Abbreviated Journal |
AC |
| Volume |
144 |
Issue |
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Pages |
104614 |
| Keywords |
Sewer Defect Classification; Vision Transformers; Sinkhorn-Knopp; Convolutional Neural Networks; Closed-Circuit Television; Sewer Inspection |
| Abstract |
A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points. |
| Address |
Dec 2022 |
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| Notes |
HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ BME2022c |
Serial |
3780 |
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| Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey |
| Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Archaeological Science |
Abbreviated Journal |
JArchSci |
| Volume |
148 |
Issue |
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Pages |
105654 |
| Keywords |
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| Abstract |
This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. |
| Address |
December 2022 |
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| Notes |
MSIAU; MACO; 600.167 |
Approved |
no |
| Call Number |
Admin @ si @ BOL2022 |
Serial |
3753 |
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| Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
| Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
160 |
Issue |
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Pages |
43-49 |
| Keywords |
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| Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
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Elsevier |
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| Notes |
DAG; 600.121; 600.162; 602.230 |
Approved |
no |
| Call Number |
Admin @ si @ SFK2022 |
Serial |
3736 |
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| Author |
German Barquero; Johnny Nuñez; Sergio Escalera; Zhen Xu; Wei-Wei Tu; Isabelle Guyon |
| Title |
Didn’t see that coming: a survey on non-verbal social human behavior forecasting |
Type |
Conference Article |
| Year |
2022 |
Publication |
Understanding Social Behavior in Dyadic and Small Group Interactions |
Abbreviated Journal |
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| Volume |
173 |
Issue |
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Pages |
139-178 |
| Keywords |
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| Abstract |
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises
methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarized and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues. |
| Address |
Virtual; June 2022 |
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PMLR |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BNE2022 |
Serial |
3766 |
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| Author |
Adam Fodor; Rachid R. Saboundji; Julio C. S. Jacques Junior; Sergio Escalera; David Gallardo Pujol; Andras Lorincz |
| Title |
Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures |
Type |
Conference Article |
| Year |
2022 |
Publication |
Understanding Social Behavior in Dyadic and Small Group Interactions |
Abbreviated Journal |
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| Volume |
173 |
Issue |
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Pages |
218-241 |
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| Abstract |
Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures{’} preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots. |
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PMLR |
| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ FSJ2022 |
Serial |
3769 |
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| Author |
Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu |
| Title |
NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results |
Type |
Conference Article |
| Year |
2022 |
Publication |
Understanding Social Behavior in Dyadic and Small Group Interactions |
Abbreviated Journal |
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| Volume |
191 |
Issue |
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Pages |
24-37 |
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| Abstract |
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. |
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PMLR |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ CCB2022 |
Serial |
3802 |
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| Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
| Title |
Human Body Pose Estimation in Multi-view Environments |
Type |
Book Chapter |
| Year |
2022 |
Publication |
ICT Applications for Smart Cities. Intelligent Systems Reference Library |
Abbreviated Journal |
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| Volume |
224 |
Issue |
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Pages |
79-99 |
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| Abstract |
This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches. |
| Address |
September 2022 |
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Springer |
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ISRL |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-3-031-06306-0 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MSIAU; MACO |
Approved |
no |
| Call Number |
Admin @ si @ CSV2022b |
Serial |
3810 |
| Permanent link to this record |
| |
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| |
| Author |
Henry Velesaca; Patricia Suarez; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez; Angel Morera |
| Title |
Video Analytics in Urban Environments: Challenges and Approaches |
Type |
Book Chapter |
| Year |
2022 |
Publication |
ICT Applications for Smart Cities |
Abbreviated Journal |
|
| Volume |
224 |
Issue |
|
Pages |
101-121 |
| Keywords |
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| Abstract |
This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field. |
| Address |
September 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
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 |
ISRL |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
978-3-031-06306-0 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
MSIAU; MACO |
Approved |
no |
| Call Number |
Admin @ si @ VSC2022 |
Serial |
3811 |
| Permanent link to this record |
