| Records |
| 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 |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla |
| Title |
Thermal Image Super-Resolution: A Novel Unsupervised Approach |
Type |
Conference Article |
| Year |
2022 |
Publication |
International Joint Conference on Computer Vision, Imaging and Computer Graphics |
Abbreviated Journal |
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| Volume |
1474 |
Issue |
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Pages |
495–506 |
| Keywords |
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| Abstract |
This paper proposes the use of a CycleGAN architecture for thermal image super-resolution under a transfer domain strategy, where middle-resolution images from one camera are transferred to a higher resolution domain of another camera. The proposed approach is trained with a large dataset acquired using three thermal cameras at different resolutions. An unsupervised learning process is followed to train the architecture. Additional loss function is proposed trying to improve results from the state of the art approaches. Following the first thermal image super-resolution challenge (PBVS-CVPR2020) evaluations are performed. A comparison with previous works is presented showing the proposed approach reaches the best results. |
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VISIGRAPP |
| Notes |
MSIAU; 600.130 |
Approved |
no |
| Call Number |
Admin @ si @ RSV2022d |
Serial |
3776 |
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| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Jin Kim; Dogun Kim; Zhihao Li; Yingchun Jian; Bo Yan; Leilei Cao; Fengliang Qi; Hongbin Wang Rongyuan Wu; Lingchen Sun; Yongqiang Zhao; Lin Li; Kai Wang; Yicheng Wang; Xuanming Zhang; Huiyuan Wei; Chonghua Lv; Qigong Sun; Xiaolin Tian; Zhuang Jia; Jiakui Hu; Chenyang Wang; Zhiwei Zhong; Xianming Liu; Junjun Jiang |
| Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2022 |
Type |
Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
418-426 |
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| Abstract |
This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. |
| Address |
New Orleans; USA; June 2022 |
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Conference |
CVPRW |
| Notes |
MSIAU; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ RSV2022c |
Serial |
3775 |
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| Author |
Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim |
| Title |
Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 |
Type |
Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
390-398 |
| Keywords |
Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers |
| Abstract |
This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. |
| Address |
New Orleans; USA; June 2022 |
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Conference |
CVPRW |
| Notes |
MSIAU; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ RMV2022 |
Serial |
3774 |
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| Author |
David Berga; Xavier Otazu |
| Title |
A neurodynamic model of saliency prediction in v1 |
Type |
Journal Article |
| Year |
2022 |
Publication |
Neural Computation |
Abbreviated Journal |
NEURALCOMPUT |
| Volume |
34 |
Issue |
2 |
Pages |
378-414 |
| Keywords |
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| Abstract |
Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex. |
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Conference |
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| Notes |
NEUROBIT; 600.128; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ BeO2022 |
Serial |
3696 |
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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 |
| Call Number |
Admin @ si @ MMB2022 |
Serial |
3701 |
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| Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
| Title |
Out-of-Vocabulary Challenge Report |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
359–375 |
| Keywords |
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| Abstract |
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. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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Conference |
ECCVW |
| Notes |
DAG; 600.155; 302.105; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ GMB2022 |
Serial |
3771 |
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| Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
| Title |
Multi-Modal Aerial View Object Classification Challenge Results – PBVS 2022 |
Type |
Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
350-358 |
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| Abstract |
This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. |
| Address |
New Orleans; USA; June 2022 |
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Conference |
CVPRW |
| Notes |
MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ LNS2022 |
Serial |
3768 |
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| Author |
Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
| Title |
MUST-VQA: MUltilingual Scene-text VQA |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
345–358 |
| Keywords |
Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
| Abstract |
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. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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Conference |
ECCVW |
| Notes |
DAG; 302.105; 600.155; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ VBM2022 |
Serial |
3770 |
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| Author |
Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados |
| Title |
Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis |
Type |
Conference Article |
| Year |
2022 |
Publication |
Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
Abbreviated Journal |
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| Volume |
13424 |
Issue |
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Pages |
336-348 |
| Keywords |
Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk |
| Abstract |
Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. |
| Address |
June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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LNCS |
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IGS |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ BFC2022 |
Serial |
3738 |
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| Author |
Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai |
| Title |
Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks |
Type |
Conference Article |
| Year |
2022 |
Publication |
17th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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13804 |
Issue |
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Pages |
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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LNCS |
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978-3-031-25068-2 |
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ECCV-TiE |
| Notes |
DAG; 600.162; 600.140; 110.312 |
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no |
| Call Number |
Admin @ si @ GBC2022 |
Serial |
3795 |
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| Author |
Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera |
| Title |
Multi-varied Cumulative Alignment for Domain Adaptation |
Type |
Conference Article |
| Year |
2022 |
Publication |
6th International Conference on Image Analysis and Processing |
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13232 |
Issue |
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Pages |
324–334 |
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Domain Adaptation; Computer vision; Neural networks |
| Abstract |
Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca). |
| Address |
Indonesia; October 2022 |
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LNCS |
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ICIAP |
| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ OAS2022 |
Serial |
3777 |
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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 |
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HuPBA; no menciona |
Approved |
no |
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Admin @ si @ FSJ2022 |
Serial |
3769 |
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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 |
| Title |
The RPM3D Project: 3D Kinematics for Remote Patient Monitoring |
Type |
Conference Article |
| Year |
2022 |
Publication |
Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
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| Volume |
13424 |
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Pages |
217-226 |
| Keywords |
Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics |
| Abstract |
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. |
| Address |
June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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IGS |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Admin @ si @ FBC2022 |
Serial |
3739 |
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| Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
| Title |
Waste Classification with Small Datasets and Limited Resources |
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 |
185-203 |
| Keywords |
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| Abstract |
Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network. |
| Address |
September 2022 |
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Springer |
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978-3-031-06306-0 |
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LAMP |
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no |
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Admin @ si @ |
Serial |
3813 |
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