| Records |
Author  |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
| Title |
Articulated Particle Filter for Hand Tracking |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3581 - 3585 |
| Keywords |
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| Abstract |
This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
| Address |
Tsukuba Science City, Japan |
| Corporate Author |
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Thesis |
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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 Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RMG2012 |
Serial |
2031 |
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| Author |
German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
| Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
12 |
Issue |
|
Pages |
227-241 |
| Keywords |
SYNTHIA; Virtual worlds; Autonomous Driving |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
Gabriela Csurka |
| 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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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.085; 600.082; 600.076; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSV2017 |
Serial |
2882 |
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| Author |
German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
| Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
Type |
Conference Article |
| Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3234-3243 |
| Keywords |
Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
| Address |
Las Vegas; USA; June 2016 |
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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 |
CVPR |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSM2016 |
Serial |
2739 |
| Permanent link to this record |
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| Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
| Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
Type |
Conference Article |
| Year |
2015 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
231 - 238 |
| Keywords |
Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
| Abstract |
Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
| Address |
Hawaii; January 2015 |
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Medium |
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| Area |
ACDC |
Expedition |
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Conference |
WACV |
| Notes |
ADAS; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ RRG2015 |
Serial |
2499 |
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| Author |
Gholamreza Anbarjafari; Sergio Escalera |
| Title |
Human-Robot Interaction: Theory and Application |
Type |
Book Whole |
| Year |
2018 |
Publication |
Human-Robot Interaction: Theory and Application |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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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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ISBN |
978-1-78923-316-2 |
Medium |
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Expedition |
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Conference |
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| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ AnE2018 |
Serial |
3216 |
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| Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
| Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type |
Conference Article |
| Year |
2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
153-158 |
| Keywords |
transcription of ciphers; hand-written text recognition of symbols; graphic signs |
| Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
| Address |
Amsterdam, Netherlands, June 20-22, 2022 |
| Corporate Author |
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Place of Publication |
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Summary Language |
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Original 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 |
HystoCrypt |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ MBS2022 |
Serial |
3731 |
| Permanent link to this record |
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| Author |
Gioacchino Vino; Angel Sappa |
| Title |
Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach |
Type |
Conference Article |
| Year |
2013 |
Publication |
10th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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| Volume |
7950 |
Issue |
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Pages |
354-363 |
| Keywords |
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| Abstract |
This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. |
| Address |
Póvoa de Varzim; Portugal; June 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
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 Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-39093-7 |
Medium |
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| Area |
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Expedition |
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Conference |
ICIAR |
| Notes |
ADAS; 600.055 |
Approved |
no |
| Call Number |
Admin @ si @ ViS2013 |
Serial |
2562 |
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| Author |
Giovanni Maria Farinella; Petia Radeva; Jose Braz |
| Title |
Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications |
Type |
Book Whole |
| Year |
2020 |
Publication |
Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020 |
Abbreviated Journal |
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| Volume |
4 |
Issue |
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Pages |
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Medium |
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Expedition |
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Conference |
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| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ FRB2020a |
Serial |
3546 |
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| Author |
Giovanni Maria Farinella; Petia Radeva; Jose Braz |
| Title |
Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications |
Type |
Book Whole |
| Year |
2020 |
Publication |
Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020 |
Abbreviated Journal |
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| Volume |
5 |
Issue |
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Pages |
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Medium |
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Expedition |
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Conference |
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MILAB |
Approved |
no |
| Call Number |
Admin @ si @ FRB2020b |
Serial |
3547 |
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| Author |
Giovanni Maria Farinella; Petia Radeva; Jose Braz; Kadi Bouatouch |
| Title |
Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Volume 4) |
Type |
Book Whole |
| Year |
2021 |
Publication |
Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2021 |
Abbreviated Journal |
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| Volume |
4 |
Issue |
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Pages |
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| Keywords |
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| Abstract |
This book contains the proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), endorsed by the International Association for Pattern Recognition (IAPR), and in cooperation with the ACM Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH), the European Association for Computer Graphics (EUROGRAPHICS), the EUROGRAPHICS Portuguese Chapter, the VRVis Center for Virtual Reality and Visualization Forschungs-GmbH, the French Association for Computer Graphics (AFIG), and the Society for Imaging Science and Technology (IS&T). The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthy of being disseminated to the interested research audiences. VISIGRAPP 2021 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions. This year VISIGRAPP was, exceptionally, held as a web-based event, due to the COVID-19 pandemic, from 8 – 10 February. We received a high number of paper submissions for this edition of VISIGRAPP, 371 in total, with contributions from 52 countries. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a hierarchical process of double-blind evaluation where each paper was reviewed by two to six experts from the International Program Committee (IPC). The IPC selected for oral presentation and for publication as full papers 12 papers from GRAPP, 8 from HUCAPP, 11 papers from IVAPP, and 56 papers from VISAPP, which led to a result for the full-paper acceptance ratio of 24% and a high-quality program. Apart from the above full papers, the conference program also features 118 short papers and 67 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, SCOPUS, DBLP, Semantic Scholar, Google Scholar, EI and Microsoft Academic, will help the Computer Vision, Imaging, Visualization, Computer Graphics and Human-Computer Interaction communities to find interesting research work. Moreover, we are proud to inform that the program also includes three plenary keynote lectures, given by internationally distinguished researchers, namely Federico Tombari (Google and Technical University of Munich, Germany), Dieter Schmalstieg (Graz University of Technology, Austria) and Nathalie Henry Riche (Microsoft Research, United States), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields. Furthermore, a short list of the presented papers will be selected to be extended into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2021 in the CCIS series. Moreover, a short list of presented papers will be selected for publication of extended and revised versions in a special issue of the Springer Nature Computer Science journal. All papers presented at this conference will be available at the SCITEPRESS Digital Library. Three awards are delivered at the closing session, to recognize the best conference paper, the best student paper and the best poster for each of the four conferences. There is also an award for best industrial paper to be delivered at the closing session for VISAPP. We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made it possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Finally, we gratefully acknowledge the professional support of the INSTICC team for all organizational processes, especially given the need to introduce online streaming, forum management, direct messaging facilitation and other web-based activities in order to make it possible for VISIGRAPP 2021 authors to present their work and share ideas with colleagues in spite of the logistic difficulties caused by the current pandemic situation. We wish you all an exciting conference. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org |
| Address |
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Thesis |
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Place of Publication |
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Original 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 |
VISIGRAPP |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ FRB2021a |
Serial |
3627 |
| Permanent link to this record |
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| Author |
Giovanni Maria Farinella; Petia Radeva; Jose Braz; Kadi Bouatouch |
| Title |
Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – (Volume 5) |
Type |
Book Whole |
| Year |
2021 |
Publication |
Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – VISIGRAPP 2021 |
Abbreviated Journal |
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| Volume |
5 |
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
This book contains the proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), endorsed by the International Association for Pattern Recognition (IAPR), and in cooperation with the ACM Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH), the European Association for Computer Graphics (EUROGRAPHICS), the EUROGRAPHICS Portuguese Chapter, the VRVis Center for Virtual Reality and Visualization Forschungs-GmbH, the French Association for Computer Graphics (AFIG), and the Society for Imaging Science and Technology (IS&T). The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthy of being disseminated to the interested research audiences. VISIGRAPP 2021 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions. This year VISIGRAPP was, exceptionally, held as a web-based event, due to the COVID-19 pandemic, from 8 – 10 February. We received a high number of paper submissions for this edition of VISIGRAPP, 371 in total, with contributions from 52 countries. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a hierarchical process of double-blind evaluation where each paper was reviewed by two to six experts from the International Program Committee (IPC). The IPC selected for oral presentation and for publication as full papers 12 papers from GRAPP, 8 from HUCAPP, 11 papers from IVAPP, and 56 papers from VISAPP, which led to a result for the full-paper acceptance ratio of 24% and a high-quality program. Apart from the above full papers, the conference program also features 118 short papers and 67 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, SCOPUS, DBLP, Semantic Scholar, Google Scholar, EI and Microsoft Academic, will help the Computer Vision, Imaging, Visualization, Computer Graphics and Human-Computer Interaction communities to find interesting research work. Moreover, we are proud to inform that the program also includes three plenary keynote lectures, given by internationally distinguished researchers, namely Federico Tombari (Google and Technical University of Munich, Germany), Dieter Schmalstieg (Graz University of Technology, Austria) and Nathalie Henry Riche (Microsoft Research, United States), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields. Furthermore, a short list of the presented papers will be selected to be extended into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2021 in the CCIS series. Moreover, a short list of presented papers will be selected for publication of extended and revised versions in a special issue of the Springer Nature Computer Science journal. All papers presented at this conference will be available at the SCITEPRESS Digital Library. Three awards are delivered at the closing session, to recognize the best conference paper, the best student paper and the best poster for each of the four conferences. There is also an award for best industrial paper to be delivered at the closing session for VISAPP. We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made it possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Finally, we gratefully acknowledge the professional support of the INSTICC team for all organizational processes, especially given the need to introduce online streaming, forum management, direct messaging facilitation and other web-based activities in order to make it possible for VISIGRAPP 2021 authors to present their work and share ideas with colleagues in spite of the logistic difficulties caused by the current pandemic situation. We wish you all an exciting conference. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org. |
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Expedition |
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Conference |
VISIGRAPP |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ FRB2021b |
Serial |
3628 |
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| Author |
Gisel Bastidas-Guacho; Patricio Moreno; Boris X. Vintimilla; Angel Sappa |
| Title |
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches |
Type |
Conference Article |
| Year |
2023 |
Publication |
13th International Conference on Pattern Recognition Systems |
Abbreviated Journal |
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| Volume |
14234 |
Issue |
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Pages |
25–36 |
| Keywords |
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| Abstract |
Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. Then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion. |
| Address |
Guayaquil; Ecuador; July 2023 |
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Conference |
ICPRS |
| Notes |
MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ BMV2023 |
Serial |
3932 |
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| Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
| Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
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Conference Article |
| Year |
2022 |
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Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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3-12 |
| Keywords |
N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
| Abstract |
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ GBS2022 |
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3733 |
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| Author |
Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva |
| Title |
CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation |
Type |
Journal Article |
| Year |
2021 |
Publication |
Computers in Biology and Medicine |
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CBM |
| Volume |
136 |
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104689 |
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The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. |
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MILAB; no menciona |
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Admin @ si @ PDR2021 |
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3635 |
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Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva |
| Title |
CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation |
Type |
Journal Article |
| Year |
2021 |
Publication |
Computer Methods and Programs in Biomedicine |
Abbreviated Journal |
CMPB |
| Volume |
198 |
Issue |
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Pages |
105792 |
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Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software. |
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MILAB; no proj |
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Admin @ si @ PRR2021 |
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3530 |
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