|
Records |
Links |
|
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 |
|
|
|
Volume |
224 |
Issue |
|
Pages |
79-99 |
|
|
Keywords |
|
|
|
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 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
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 @ CSV2022b |
Serial |
3810 |
|
Permanent link to this record |
|
|
|
|
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 |
|
|
|
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 |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
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 |
|
|
|
|
Author |
Angel Sappa (ed) |
|
|
Title |
ICT Applications for Smart Cities |
Type |
Book Whole |
|
Year |
2022 |
Publication |
ICT Applications for Smart Cities |
Abbreviated Journal |
|
|
|
Volume |
224 |
Issue |
|
Pages |
|
|
|
Keywords |
Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing |
|
|
Abstract |
Part of the book series: Intelligent Systems Reference Library (ISRL)
This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:
• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes
The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field. |
|
|
Address |
September 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
Angel Sappa |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
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 @ Sap2022 |
Serial |
3812 |
|
Permanent link to this record |
|
|
|
|
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 |
|
|
|
Volume |
224 |
Issue |
|
Pages |
185-203 |
|
|
Keywords |
|
|
|
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 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
ISRL |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-3-031-06306-0 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP |
Approved |
no |
|
|
Call Number |
Admin @ si @ |
Serial |
3813 |
|
Permanent link to this record |
|
|
|
|
Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
|
|
Title |
Frequency-based Enhancement Network for Efficient Super-Resolution |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
10 |
Issue |
|
Pages |
57383-57397 |
|
|
Keywords |
Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution |
|
|
Abstract |
Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet |
|
|
Address |
18 May 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ISE |
Approved |
no |
|
|
Call Number |
Admin @ si @ BRF2022a |
Serial |
3747 |
|
Permanent link to this record |
|
|
|
|
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 |
|
|
|
Volume |
|
Issue |
|
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 |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
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 |
|
|
|
|
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 |
|
Pages |
108641 |
|
|
Keywords |
|
|
|
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 |
|
Thesis |
|
|
|
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.162; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RGR2022 |
Serial |
3729 |
|
Permanent link to this record |
|
|
|
|
Author |
Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier |
|
|
Title |
Modeling, Recognizing, and Explaining Apparent Personality from Videos |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
|
|
Volume |
13 |
Issue |
2 |
Pages |
894-911 |
|
|
Keywords |
|
|
|
Abstract |
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future. |
|
|
Address |
1 April-June 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HuPBA; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ EKS2022 |
Serial |
3406 |
|
Permanent link to this record |
|
|
|
|
Author |
Mohamed Ali Souibgui; Y.Kessentini |
|
|
Title |
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
44 |
Issue |
3 |
Pages |
1180-1191 |
|
|
Keywords |
|
|
|
Abstract |
Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. |
|
|
Address |
1 March 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 602.230; 600.121; 600.140 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SoK2022 |
Serial |
3454 |
|
Permanent link to this record |
|
|
|
|
Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier |
|
|
Title |
Effects of Non-Driving Related Tasks during Self-Driving mode |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
|
|
Volume |
23 |
Issue |
2 |
Pages |
1391-1399 |
|
|
Keywords |
|
|
|
Abstract |
Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study. |
|
|
Address |
Feb. 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; 600.139; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MHE2022 |
Serial |
3468 |
|
Permanent link to this record |
|
|
|
|
Author |
Jun Wan; Chi Lin; Longyin Wen; Yunan Li; Qiguang Miao; Sergio Escalera; Gholamreza Anbarjafari; Isabelle Guyon; Guodong Guo; Stan Z. Li |
|
|
Title |
ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Cybernetics |
Abbreviated Journal |
TCIBERN |
|
|
Volume |
52 |
Issue |
5 |
Pages |
3422-3433 |
|
|
Keywords |
|
|
|
Abstract |
The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. |
|
|
Address |
May 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HUPBA; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ WLW2022 |
Serial |
3522 |
|
Permanent link to this record |
|
|
|
|
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 |
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Science Direct |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MSIAU; MACO |
Approved |
no |
|
|
Call Number |
Admin @ si @ ROS2022 |
Serial |
3750 |
|
Permanent link to this record |
|
|
|
|
Author |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
|
|
Title |
LDC: Lightweight Dense CNN for Edge Detection |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
10 |
Issue |
|
Pages |
68281-68290 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
|
|
Address |
27 June 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MSIAU; MACO; 600.160; 600.167 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SPS2022 |
Serial |
3751 |
|
Permanent link to this record |
|
|
|
|
Author |
Marc Masana; Xialei Liu; Bartlomiej Twardowski; Mikel Menta; Andrew Bagdanov; Joost Van de Weijer |
|
|
Title |
Class-incremental learning: survey and performance evaluation |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
For future learning systems incremental learning is desirable, because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning. The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one. Incremental learning of deep neural networks has seen explosive growth in recent years. Initial work focused on task incremental learning, where a task-ID is provided at inference time. Recently we have seen a shift towards class-incremental learning where the learner must classify at inference time between all classes seen in previous tasks without recourse to a task-ID. In this paper, we provide a complete survey of existing methods for incremental learning, and in particular we perform an extensive experimental evaluation on twelve class-incremental methods. We consider several new experimental scenarios, including a comparison of class-incremental methods on multiple large-scale datasets, investigation into small and large domain shifts, and comparison on various network architectures. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MLT2022 |
Serial |
3538 |
|
Permanent link to this record |
|
|
|
|
Author |
Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach |
|
|
Title |
First in Vivo Computer-Aided Diagnosis of Colorectal Polyps using White Light Endoscopy |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Endoscopy |
Abbreviated Journal |
END |
|
|
Volume |
54 |
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
2022/04/14 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Georg Thieme Verlag KG |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ISE |
Approved |
no |
|
|
Call Number |
Admin @ si @ GTC2022a |
Serial |
3746 |
|
Permanent link to this record |